Determining a driver score based on the driver&#39;s response to autonomous features of a vehicle

ABSTRACT

Systems and methods are disclosed for generating vehicle insurance rates based on driver-independent variables and/or driver-dependent variables. Vehicle insurance rates may additionally or alternatively be based on changes in the level of autonomy of vehicles. In some embodiments, a density of vehicles near a target vehicle may be tracked. Vehicle insurance rates may be determined based on the vehicle density. Furthermore, systems and methods are disclosed for analyzing a driver&#39;s use of autonomous vehicle features and/or the driver&#39;s maintenance of the autonomous vehicle. The driver may also be taught certain driving skills by enabling vehicle teaching features. The driver&#39;s response to these teaching features may be monitored, and a reward or recommendation may be generated and provided to the driver based on the driver&#39;s response.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of pending U.S. patentapplication Ser. No. 14/184,272 entitled “Insurance System for Analysisof Autonomous Driving” and filed on Feb. 19, 2014, which is herebyincorporated by reference in its entirety.

TECHNICAL FIELD

Aspects of the disclosure generally relate to the analysis of vehicledriving data of vehicles that have engaged autonomous driving featuresfor the purposes of determining aspects of vehicle insurance, and forteaching driving skills to drivers of autonomous vehicles.

BACKGROUND

Many vehicles include sensors and internal computer systems designed tomonitor and control vehicle operations, driving conditions, and drivingfunctions. Advanced vehicles systems can perform such tasks as detectingand correcting a loss of traction on an icy road, self-parking, ordetecting an imminent collision or unsafe driving condition andautomatically making evasive maneuvers. Additionally, vehicles caninclude autonomous driving systems that assume all or part of real-timedriving functions to operate the vehicle without real-time input from ahuman operator.

Many vehicles also include communication systems designed to send andreceive information from inside or outside the vehicle. Such informationcan include, for example, vehicle operational data, driving conditions,and communications from other vehicles or systems. For example, aBluetooth system may enable communication between the vehicle and thedriver's mobile phone. Telematics systems may be configured to accessvehicle computers and sensor data, including on-board diagnosticssystems (OBD), and transmit the data to a display within the vehicle, apersonal computer or mobile device, or to a centralized data processingsystem. Additionally, vehicle-to-vehicle (V2V) communication systems canbe used to send and receive information from other nearby vehicles. Dataobtained from vehicle sensors, Telematics systems, OBD systems, and V2Vsystems, have been used for a variety of purposes, includingmaintenance, diagnosis, and analysis.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure relate to systems, methods, and computingdevices for determining, by a computing device, a property of aninsurance policy for a semi-autonomous vehicle. The property of theinsurance policy may comprise at least one of a premium, a deductible, acoverage term, and a coverage amount. The property of the insurancepolicy may be based at least on a driver-independent variable of thesemi-autonomous vehicle and a driver-dependent variable. Thedriver-independent variable of the semi-autonomous vehicle may comprisea Vehicle Identification Number (VIN). The driver-dependent variable maycomprise an age of a driver of the semi-autonomous vehicle or a locationof residence of the driver of the semi-autonomous vehicle. In someaspects, the property of the insurance policy may be based on aplurality of driver-independent variables including thedriver-independent variable and a plurality of driver-dependentvariables including the driver-dependent variable.

In response to a determination that a level of autonomy of thesemi-autonomous vehicle has changed, a value of the property of theinsurance policy may be changed. Changing the value of the property ofthe insurance policy may comprise determining that the level of autonomyof the semi-autonomous vehicle has increased, and increasing a weightfor the driver-independent variable of the semi-autonomous vehicle ordecreasing a weight for the driver-dependent variable. Changing thevalue of the property may also comprise determining the value of theproperty of the insurance policy based on at least one of the increasedweight for the driver-independent variable of the semi-autonomousvehicle and the decreased weight for the driver-dependent variable.

Changing the value of the property of the insurance policy may comprisedetermining that the level of autonomy of the semi-autonomous vehiclehas decreased, and decreasing a weight for the driver-independentvariable of the semi-autonomous vehicle or increasing a weight for thedriver-dependent variable. Changing the value of the property may alsocomprise determining the value of the property of the insurance policybased on at least one of the decreased weight for the driver-independentvariable of the semi-autonomous vehicle and the increased weight for thedriver-dependent variable.

In some aspects, systems, methods, and computing devices may determinethat a classification of the vehicle has changed from semi-autonomous tocompletely autonomous. In response to determining that theclassification of the vehicle has changed from semi-autonomous tocompletely autonomous, the value of the property of the insurance policymay be changed based on the driver-independent variable and not thedriver-dependent variable.

Systems, methods, and computing devices described herein may alsoretrieve, from a database, a vehicle identifier of a semi-autonomous orcompletely autonomous vehicle, and determine a property of an insurancepolicy for the semi-autonomous or completely autonomous vehicle. Aspreviously discussed, the property of the insurance policy may compriseat least one of a premium, a deductible, a coverage term, and a coverageamount. The property of the insurance policy may be based on the vehicleidentifier of the semi-autonomous or completely autonomous vehicle. Theproperty of the insurance policy might not be based on anydriver-dependent variables. As previously discussed, the vehicleidentifier may comprise a VIN. Additionally or alternatively, theproperty of the insurance policy may be based on a plurality ofdriver-independent variables including the vehicle identifier.

Aspects of the disclosure relate to systems, methods, and computingdevices for determining, by a computing device, data identifyingvehicles within a predetermined distance of a first vehicle. Thepredetermined distance of the first vehicle may comprise a predetermineddistance of the first vehicle along a path of the first vehicle.

The systems, methods, and computing devices may determine a density ofvehicles within the predetermined distance of the first vehicle based onthe determined data identifying the vehicles. Determining the density ofvehicles within the predetermined distance of the first vehicle maycomprise determining a density of completely autonomous vehicles withinthe predetermined distance of the first vehicle. A property of aninsurance policy for the first vehicle may be generated based on thedetermined density of vehicles. Additionally or alternatively,determining the density of vehicles within the predetermined distance ofthe first vehicle may comprise determining a density of completelyautonomous vehicles within the predetermined distance of the firstvehicle and a density of semi-autonomous vehicles within thepredetermined distance of the first vehicle. Additionally oralternatively, determining the density of vehicles within thepredetermined distance of the first vehicle may comprise determining adensity of completely autonomous vehicles within the predetermineddistance of the first vehicle, a density of semi-autonomous vehicleswithin the predetermined distance of the first vehicle, and a density ofnon-autonomous vehicles within the predetermined distance of the firstvehicle.

The systems, methods, and computing devices may sense, using a pluralityof sensors on the first vehicle, a number of the vehicles within thepredetermined distance of the first vehicle. The plurality of sensorsmay comprise at least two of the following: a camera, a proximitysensor, a vehicle-to-vehicle communication device, and avehicle-to-infrastructure communication device. In some aspects,determining the data identifying vehicles within the predetermineddistance of the first vehicle may comprise generating the data based onthe sensed number of vehicles.

The systems, methods, and computing devices may send, by the computingdevice, the data identifying vehicles within the predetermined distanceof the first vehicle to a remote driving analysis computing device. Theremote driving analysis computing device may be configured to generatethe property of the insurance policy for the first vehicle.

The systems described herein may comprise a first vehicle having avehicle computing device. The first vehicle computing device may includea first processor and first memory storing computer-executableinstructions that, when executed by the first processor, cause thevehicle computing device to generate data identifying vehicles within apredetermined distance of the first vehicle. The systems may alsocomprise a driving analysis computing device having a second processorand second memory storing computer-executable instructions that, whenexecuted by the second processor, cause the driving analysis computingdevice to determine a density of vehicles within the predetermineddistance of the first vehicle based on the generated data and generate aproperty of an insurance policy for the first vehicle based on thedetermined density of vehicles.

The first memory described herein may store computer-executableinstructions that, when executed by the first processor, cause thevehicle computing device to receive, from a plurality of sensors on thefirst vehicle, a sensed number of vehicles within the predetermineddistance of the first vehicle. Generating the data identifying vehicleswithin the predetermined distance of the first vehicle may comprisegenerating the data based on the sensed number of vehicles. Theplurality of sensors may comprise at least two of the following: acamera, a proximity sensor, a vehicle-to-vehicle communication device,and a vehicle-to-infrastructure communication device. The first memorydescribed herein may also store computer-executable instructions that,when executed by the first processor, cause the vehicle computing deviceto send the data identifying vehicles within the predetermined distanceof the first vehicle to the driving analysis computing device.

Aspects of the disclosure relate to systems, methods, and computingdevices for sending an instruction to a vehicle to switch off anautonomous driving feature. A computing device may determine operationaldata of the vehicle after the autonomous driving feature is switchedoff. Furthermore, a value of a property of an insurance policy for thevehicle may be determined based on the operational data of the vehicleafter the autonomous feature is switched off. As previously discussed,the property of the insurance policy may comprise at least one of apremium, a deductible, a coverage term, and a coverage amount.

In some aspects, the property of the insurance policy may comprise aninsurance quote for the vehicle, and determining the value of theproperty of the insurance policy may comprise determining the value ofthe insurance quote for the vehicle based on the operational data of thevehicle after the autonomous feature is switched off. The operationaldata may indicate a driver of the vehicle's response to the autonomousfeature being switched off.

Determining the value of the property of the insurance policy for thevehicle may be based on the operational data of the vehicle after theautonomous feature is switched off and a maintenance history for thevehicle. Furthermore, the maintenance history may comprise at least oneof a software upgrade to a computing device of the vehicle and aresponse to an illegal access to software of the computing device.

Determining the value of the property may be based on other information.For example, determining the value of the property of the insurancepolicy for the vehicle may be based on the operational data of thevehicle after the autonomous feature is switched off and use of anautonomous vehicle lane. Determining the value of the property of theinsurance policy for the vehicle may additionally or alternatively bebased on the operational data of the vehicle after the autonomousfeature is switched off and use of an autonomous parallel parkingfeature of the vehicle. Determining the operational data of the vehicleafter the autonomous driving feature is switched off may comprisedetermining a history of a driver of the vehicle's response to theautonomous driving feature being switched off over a period of time.

A system for performing the above-described steps is described herein.For example, a system may comprise a vehicle computing device and adriving analysis computing device. The driving analysis computing devicemay include a processor and memory storing computer-executableinstructions that, when executed by the processor, cause the drivinganalysis computing device to send an instruction to the vehiclecomputing device of a vehicle to switch off an autonomous drivingfeature. The driving analysis computing device may also determineoperational data of the vehicle after the autonomous driving feature isswitched off, and determine a value of a property of an insurance policyfor the vehicle based on the operational data of the vehicle after theautonomous feature is switched off. The system may perform additionalsteps as previously discussed and as described herein.

Aspects of the disclosure relate to systems, methods, and computingdevices for switching off an autonomous feature of a vehicle in responseto a determination that an environmental condition of the vehicle issatisfied. The determination that the environmental condition of thevehicle is satisfied may comprise determining that a density of vehicleswithin a predetermined distance of the vehicle is below a threshold. Insome examples, prior to switching off the autonomous feature of thevehicle, the method may include determining that the driver of thevehicle has enabled a teaching feature of the vehicle. The determinationthat the environmental condition of the vehicle is satisfied may beperformed at a first time, and the method may further comprise switchingon the autonomous feature of the vehicle in response to a determinationthat the environmental condition of the vehicle is not satisfied at asecond time after the first time.

A computing device may determine operational data of the vehicle afterthe autonomous feature of the vehicle is switched off, and theoperational data of the vehicle may indicate a driver of the vehicle'sresponse to the autonomous feature of the vehicle being switched off. Ascore based on the operational data of the vehicle after the autonomousfeature is switched off may be generated. If the score exceeds athreshold, a reward may be provided to the driver of the vehicle. If thescore does not exceed a threshold, a recommended response for the driverof the vehicle may be generated. The recommended response may beprovided to the driver of the vehicle while the driver is in thevehicle. In some aspects, a value of a property of an insurance policyfor the vehicle may be determined based on the generated score.

A system for performing the above-described steps is described herein.For example, the system may comprise a vehicle having a vehiclecomputing device, the vehicle computing device including a firstprocessor and first memory storing computer-executable instructionsthat, when executed by the first processor, cause the vehicle computingdevice to switch off an autonomous feature of a vehicle in response to adetermination that an environmental condition of the vehicle issatisfied. The vehicle computing device may also determine operationaldata of the vehicle after the autonomous feature of the vehicle isswitched off, the operational data of the vehicle indicating a driver ofthe vehicle's response to the autonomous feature of the vehicle beingswitched off. The system may also comprise a driving analysis computingdevice having a second processor and second memory storingcomputer-executable instructions that, when executed by the secondprocessor, cause the driving analysis computing device to generate ascore based on the operational data of the vehicle after the autonomousfeature is switched off. The system may perform additional steps aspreviously discussed and as described herein.

The systems described herein may include a vehicle computing device anda driving analysis computing device. The driving analysis computingdevice may comprise a processor and memory storing computer-executableinstructions that, when executed by the processor, cause the drivinganalysis computing device to perform the steps described herein.Additionally or alternatively, a non-transitory computer readable mediumstoring instructions that, when read by a computing device, may causethe computing device to perform the steps described herein.

Aspects of the present disclosure improve determination of properties ofinsurance policies for vehicles that engage in autonomous driving andimprove drivers' skills by teaching them driving techniques. Otherfeatures and advantages of the disclosure will be apparent from theadditional description provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention and theadvantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 illustrates a network environment and computing systems that maybe used to implement aspects of the disclosure.

FIG. 2 is a diagram illustrating various example components of a drivinganalysis computing device according to one or more aspects of thedisclosure.

FIG. 3 is a flow diagram illustrating an example method of analyzingvehicle driving data according to one or more aspects of the disclosure.

FIG. 4 is a flow diagram illustrating an example method of analyzingvehicle driving data according to one or more aspects of the disclosure.

FIG. 5 is a diagram illustrating one example of an autonomous drivinginsurance rating factor calculator according to one or more aspects ofthe disclosure.

FIG. 6 is a flow diagram illustrating an example method of generatingvehicle insurance rates based on driver-independent variables and/ordriver-dependent variables according to one or more aspects of thedisclosure.

FIG. 7 is a flow diagram illustrating an example method of generatingvehicle insurance rates based on changes in the level of autonomy ofvehicles according to one or more aspects of the disclosure.

FIG. 8 is a flow diagram illustrating an example method of trackingvehicle density and/or generating vehicle insurance rates based onvehicle density according to one or more aspects of the disclosure.

FIG. 9 is a flow diagram illustrating an example method of analyzing useof autonomous vehicle features and/or maintenance of autonomous vehiclesaccording to one or more aspects of the disclosure.

FIG. 10 is a flow diagram illustrating an example method of enablingvehicle teaching features and/or monitoring the driver's response toteaching features according to one or more aspects of the disclosure.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments of thedisclosure that may be practiced. It is to be understood that otherembodiments may be utilized.

As will be appreciated by one of skill in the art upon reading thefollowing disclosure, various aspects described herein may be embodiedas a method, a computer system, or a computer program product.Accordingly, those aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment or an embodiment combiningsoftware and hardware aspects. In addition, aspects may take the form ofa computing device configured to perform specified actions. Furthermore,such aspects may take the form of a computer program product stored byone or more computer-readable storage media having computer-readableprogram code, or instructions, embodied in or on the storage media. Anysuitable computer readable storage media may be utilized, including harddisks, CD-ROMs, optical storage devices, magnetic storage devices,and/or any combination thereof. In addition, various signalsrepresenting data or events as described herein may be transferredbetween a source and a destination in the form of electromagnetic wavestraveling through signal-conducting media such as metal wires, opticalfibers, and/or wireless transmission media (e.g., air and/or space).

FIG. 1 illustrates a block diagram of a computing device 101 in drivinganalysis communication system 100 that may be used according to one ormore illustrative embodiments of the disclosure. The driving analysiscomputing device 101 may have a processor 103 for controlling overalloperation of the computing device 101 and its associated components,including RAM 105, ROM 107, input/output module 109, and memory unit115. The computing device 101, along with one or more additional devices(e.g., terminals 141, 151) may correspond to any of multiple systems ordevices, such as a driving analysis computing devices or systems,configured as described herein for transmitting and receiving vehicleoperational data, analyzing vehicle operational data, determiningaspects related to vehicle insurance rating factors, includingdistance-based autonomous driving insurance rating factors, anddetermining properties of vehicle insurance policies. Vehicleoperational data can include data collected from vehicle sensors and OBDsystems. Vehicle operations can also include data pertaining to thedriver of a vehicle. Vehicle operational data can also include datapertaining to other nearby vehicles collected via, for example, V2Vcommunications. As used herein, vehicle operation data is usedinterchangeably with driving data.

The computing device 101 may additionally or alternatively be configuredto generate vehicle insurance rates based on driver-independentvariables and/or driver-dependent variables, generate vehicle insurancerates based on changes in the level of autonomy of vehicles according toone or more aspects of the disclosure, track vehicle density and/orgenerate vehicle insurance rates based on vehicle density, analyze useof autonomous vehicle features and/or maintenance of autonomousvehicles, and enable vehicle teaching features and/or monitor thedriver's response to teaching features, as will be described in furtherdetail in the examples below.

Input/Output (I/O) 109 may include a microphone, keypad, touch screen,and/or stylus through which a user of the computing device 101 mayprovide input, and may also include one or more of a speaker forproviding audio input/output and a video display device for providingtextual, audiovisual and/or graphical output. Software may be storedwithin memory unit 115 and/or other storage to provide instructions toprocessor 103 for enabling device 101 to perform various functions. Forexample, memory unit 115 may store software used by the device 101, suchas an operating system 117, application programs 119, and an associatedinternal database 121. The memory unit 115 includes one or more ofvolatile and/or non-volatile computer memory to storecomputer-executable instructions, data, and/or other information.Processor 103 and its associated components may allow the drivinganalysis system 101 to execute a series of computer-readableinstructions to transmit or receive vehicle driving data, analyzedriving data, determine driving characteristics from the driving data,and determine properties of, for example, vehicle insurance policiesusing the driving data.

The driving analysis computing device 101 may operate in a networkedenvironment 100 supporting connections to one or more remote computers,such as terminals/devices 141 and 151. Driving analysis computing device101, and related terminals/devices 141 and 151, may include devicesinstalled in vehicles, mobile devices that may travel within vehicles,or devices outside of vehicles that are configured to receive andprocess vehicle and driving data. Thus, the driving analysis computingdevice 101 and terminals/devices 141 and 151 may each include personalcomputers (e.g., laptop, desktop, or tablet computers), servers (e.g.,web servers, database servers), vehicle-based devices (e.g., on-boardvehicle computers, short-range vehicle communication systems, telematicsdevices), or mobile communication devices (e.g., mobile phones, portablecomputing devices, and the like), and may include some or all of theelements described above with respect to the driving analysis computingdevice 101. The network connections depicted in FIG. 1 include a localarea network (LAN) 125 and a wide area network (WAN) 129, and a wirelesstelecommunications network 133, but may also include other networks.When used in a LAN networking environment, the driving analysiscomputing device 101 may be connected to the LAN 125 through a networkinterface or adapter 123. When used in a WAN networking environment, thedevice 101 may include a modem 127 or other means for establishingcommunications over the WAN 129, such as network 131 (e.g., theInternet). When used in a wireless telecommunications network 133, thedevice 101 may include one or more transceivers, digital signalprocessors, and additional circuitry and software for communicating withwireless computing devices 141 (e.g., mobile phones, short-range vehiclecommunication systems, vehicle telematics devices) via one or morenetwork devices 135 (e.g., base transceiver stations) in the wirelessnetwork 133.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variousnetwork protocols such as TCP/IP, Ethernet, FTP, HTTP and the like, andof various wireless communication technologies such as GSM, CDMA, WiFi,and WiMAX, is presumed, and the various computing devices and drivinganalysis system components described herein may be configured tocommunicate using any of these network protocols or technologies.

Additionally, one or more application programs 119 used by the drivinganalysis computing device 101 may include computer executableinstructions (e.g., driving data analysis programs, drivingcharacteristic algorithms, driving and insurance policy propertiesalgorithms, vehicle insurance rating factor algorithms, driver rewardalgorithms, and driver teaching algorithms) for transmitting andreceiving vehicle driving data, determining mileage units indicatingdistances traveled by the vehicle while the vehicle was engaged inautonomous driving, determining distance-based autonomous drivinginsurance rating factors, determining various properties associated withone or more vehicle insurance policies, and performing other relatedfunctions as described herein.

Vehicle operational data may refer to information pertaining to one ormore actions or events performed by a vehicle and can include aspects ofinformation identified or determined from data collected from a vehicle.Vehicle operational data can include, for example, a vehicle speedand/or gas mileage. In addition, for example, vehicle operational datamay include an indication that the vehicle is engaged in autonomous ormanual driving, a road condition, a road-type and other operational datacollected from the vehicle.

Vehicle operational data may also include data describing theenvironment surrounding the vehicle, such as a vehicle density and typeof vehicles near the tracked vehicle (e.g., whether the vehiclessurrounding the tracked vehicle are autonomous or not). Vehicleoperational data may also include the driver's use of autonomous vehiclefeatures, the driver's maintenance of the vehicle, and/or the driver'suse of and response to autonomous driving (e.g., teaching) features.

As discussed below, a mileage unit indicating a distance traveled by thevehicle when the vehicle has engaged in autonomous driving can bedetermined from driving data collected by a vehicle sensors andtelematics device, and/or additional data received from other nearbyvehicles using vehicle-to-vehicle (V2V) communications. It should beunderstood that vehicle operational data may be associated with avehicle, a driver, or a group of vehicles or drivers engaged in socialinteraction, such as an autonomous droning relationship.

A vehicle insurance rating factor may refer to a factor which reflects arelative level of risk associated with aspects of vehicle insurance. Thevehicle insurance rating factor can be based on or more data points andbe used to determine a property of a vehicle insurance policy. Aproperty of a vehicle insurance policy can include, for example, apremium, a deductible, a coverage term, and a coverage amount. Anexample vehicle insurance rating factor of the instant disclosureincludes a distance-based autonomous driving insurance rating factor. Asused herein, a distance-based autonomous driving insurance rating factoris used synonymously with distance-based autonomous driving insurancefactor. Other exemplary rating factors include, but are not limited to,the driver's response when switching between autonomous and manualdriving features (or mode), the vehicle's maintenance history, the useof manual or automated vehicle lanes, and the use of autonomous parallelparking.

FIG. 2 is a diagram of an illustrative driving analysis system 200including two vehicles 210 and 220, a driving analysis server 250, andadditional related components. Each component shown in FIG. 2 may beimplemented in hardware, software, or a combination of the two.Additionally, each component of the driving analysis system 200 mayinclude a computing device (or system) having some or all of thestructural components described above for computing device 101.

Vehicles 210 and 220 in the driving analysis system 200 may be, forexample, automobiles, motorcycles, scooters, buses, recreationalvehicles, boats, or other vehicles for which a vehicle driving data maybe collected and analyzed. The vehicles 210 and 220 each include vehicleoperation sensors 211 and 221 capable of detecting and recording variousconditions at the vehicle and operational parameters of the vehicle. Forexample, sensors 211 and 221 may detect and store data corresponding tothe vehicle's location (e.g., GPS coordinates), time, travel time, speedand direction, rates of acceleration or braking, gas mileage, andspecific instances of sudden acceleration, braking, swerving, anddistance traveled. Sensors 211 and 221 also may detect and store datareceived from the vehicle's 210 internal systems, such as impact to thebody of the vehicle, air bag deployment, headlights usage, brake lightoperation, door opening and closing, door locking and unlocking, cruisecontrol usage, hazard lights usage, windshield wiper usage, horn usage,turn signal usage, seat belt usage, phone and radio usage within thevehicle, autonomous driving system usage, maintenance performed on thevehicle, and other data collected by the vehicle's computer systems,including the vehicle OBD.

Additional sensors 211 and 221 may detect and store the external drivingconditions, for example, external temperature, rain, snow, light levels,and sun position for driver visibility. For example, external camerasand proximity sensors 211 and 221 may detect other nearby vehicles,vehicle spacing, traffic levels, road conditions, traffic obstructions,animals, cyclists, pedestrians, and other conditions that may factorinto a driving data analysis. Sensors 211 and 221 also may detect andstore data relating to moving violations and the observance of trafficsignals and signs by the vehicles 210 and 220. Additional sensors 211and 221 may detect and store data relating to the maintenance of thevehicles 210 and 220, such as the engine status, oil level, enginecoolant temperature, odometer reading, the level of fuel in the fueltank, engine revolutions per minute (RPMs), software upgrades, and/ortire pressure.

Vehicles sensors 211 and 221 also may include cameras and/or proximitysensors capable of recording additional conditions inside or outside ofthe vehicles 210 and 220. For example, internal cameras may detectconditions such as the number of the passengers and the types ofpassengers (e.g. adults, children, teenagers, pets, etc.) in thevehicles, and potential sources of driver distraction within the vehicle(e.g., pets, phone usage, and unsecured objects in the vehicle). Sensors211 and 221 also may be configured to collect data a driver's movementsor the condition of a driver. For example, vehicles 210 and 220 mayinclude sensors that monitor a driver's movements, such as the driver'seye position and/or head position, etc. Additional sensors 211 and 221may collect data regarding the physical or mental state of the driver,such as fatigue or intoxication. The condition of the driver may bedetermined through the movements of the driver or through other sensors,for example, sensors that detect the content of alcohol in the air orblood alcohol content of the driver, such as a breathalyzer.

Certain vehicle sensors 211 and 221 also may collect informationregarding the driver's route choice, whether the driver follows a givenroute, and to classify the type of trip (e.g. commute, errand, newroute, etc.). In certain embodiments, sensors and/or cameras 211 and 221may determine when and how often the vehicles 210 and 220 stay in asingle lane or stray into other lanes. A Global Positioning System(GPS), locational sensors positioned inside the vehicles 210 and 220,and/or locational sensors or devices external to the vehicles 210 and220 may be used to determine the route, lane position, road-type (e.g.highway, entrance/exit ramp, residential area, etc.) and other vehicleposition/location data.

The data collected by vehicle sensors 211 and 221 may be stored and/oranalyzed within the respective vehicles 210 and 220, such as for examplea driving analysis computer 214, 224 integrated into the vehicle, and/ormay be transmitted to one or more external devices. For example, asshown in FIG. 2, sensor data may be transmitted via short-rangecommunication systems 212 and 222 to other nearby vehicles.Additionally, the sensor data may be transmitted via telematics devices213 and 223 to one or more remote computing devices, such as drivinganalysis server 250.

Short-range communication systems 212 and 222 are vehicle-based datatransmission systems configured to transmit vehicle operational data toother nearby vehicles, and to receive vehicle operational data fromother nearby vehicles. In some examples, communication systems 212 and222 may use the dedicated short-range communications (DSRC) protocolsand standards to perform wireless communications between vehicles. Inthe United States, 75 MHz in the 5.850-5.925 GHz band have beenallocated for DSRC systems and applications, and various other DSRCallocations have been defined in other countries and jurisdictions.However, short-range communication systems 212 and 222 need not useDSRC, and may be implemented using other short-range wireless protocolsin other examples, such as WLAN communication protocols (e.g., IEEE802.11), Bluetooth (e.g., IEEE 802.15.1), or one or more of theCommunication Access for Land Mobiles (CALM) wireless communicationprotocols and air interfaces. The vehicle-to-vehicle (V2V) transmissionsbetween the short-range communication systems 212 and 222 may be sentvia DSRC, Bluetooth, satellite, GSM infrared, IEEE 802.11, WiMAX, RFID,and/or any suitable wireless communication media, standards, andprotocols. In certain systems, short-range communication systems 212 and222 may include specialized hardware installed in vehicles 210 and 220(e.g., transceivers, antennas, etc.), while in other examples thecommunication systems 212 and 222 may be implemented using existingvehicle hardware components (e.g., radio and satellite equipment,navigation computers) or may be implemented by software running on themobile devices 215 and 225 of drivers and passengers within the vehicles210 and 220.

The range of V2V communications between vehicle communication systems212 and 222 may depend on the wireless communication standards andprotocols used, the transmission/reception hardware (e.g., transceivers,power sources, antennas), and other factors. Short-range V2Vcommunications may range from just a few feet to many miles, anddifferent types of driving behaviors may be determined depending on therange of the V2V communications. For example, V2V communications rangingonly a few feet may be sufficient for a driving analysis computingdevice 101 in one vehicle to determine that another vehicle istailgating or cut-off the vehicle, whereas longer communications mayallow the device 101 to determine additional types of driving behaviors(e.g., vehicle spacing, yielding, defensive avoidance, proper responseto a safety hazard, etc.) and driving conditions (e.g., congestion).

V2V communications also may include vehicle-to-infrastructure (V2I)communications, such as transmissions from vehicles to non-vehiclereceiving devices, for example, toll booths, rail road crossings, androad-side traffic monitoring devices. Certain V2V communication systemsmay periodically broadcast data from a vehicle 210 to any other vehicle,or other infrastructure device capable of receiving the communication,within the range of the vehicle's transmission capabilities. Forexample, a vehicle 210 may periodically broadcast (e.g., every 0.1second, every 0.5 seconds, every second, every 5 seconds, etc.) certainvehicle operation data via its short-range communication system 212,regardless of whether or not any other vehicles or reception devices arein range. In other examples, a vehicle communication system 212 mayfirst detect nearby vehicles and receiving devices, and may initializecommunication with each by performing a handshaking transaction beforebeginning to transmit its vehicle operation data to the other vehiclesand/or devices.

The types of vehicle operational data, or vehicle driving data,transmitted by vehicles 210 and 220 may depend on the protocols andstandards used for the V2V communication, the range of communications,the autonomous driving system, and other factors. In certain examples,vehicles 210 and 220 may periodically broadcast corresponding sets ofsimilar vehicle driving data, such as the location (which may include anabsolute location in GPS coordinates or other coordinate systems, and/ora relative location with respect to another vehicle or a fixed point),speed, and direction of travel. In certain examples, the nodes in a V2Vcommunication system (e.g., vehicles and other reception devices) mayuse internal clocks with synchronized time signals, and may sendtransmission times within V2V communications, so that the receiver maycalculate its distance from the transmitting node based on thedifference between the transmission time and the reception time. Thestate or usage of the vehicle's 210 controls and instruments may also betransmitted, for example, whether the vehicle is accelerating, braking,turning, and by how much, and/or which of the vehicle's instruments arecurrently activated by the driver (e.g., head lights, turn signals,hazard lights, cruise control, 4-wheel drive, traction control, etc.).Vehicle warnings such as detection by the vehicle's 210 internal systemsthat the vehicle is skidding, that an impact has occurred, or that thevehicle's airbags have been deployed, also may be transmitted in V2Vcommunications. In various other examples, any data collected by anyvehicle sensors 211 and 221 potentially may be transmitted via V2Vcommunication to other nearby vehicles or infrastructure devicesreceiving V2V communications from communication systems 212 and 222.Further, additional vehicle driving data not from the vehicle's sensors(e.g., vehicle make/model/year information, driver insuranceinformation, driver scores, etc.) may be collected from other datasources, such as a driver's or passenger's mobile device 215 or 225,driving analysis server 250, and/or another external computer system230, and transmitted using V2V communications to nearby vehicles andother transmitting and receiving devices using communication systems 212and 222.

As shown in FIG. 2, the data collected by vehicle sensors 211 and 221also may be transmitted to a driving analysis server 250, and one ormore additional external servers and devices via telematics devices 213and 223. Telematics devices 213 and 223 may be computing devicescontaining many or all of the hardware/software components as thecomputing device 101 depicted in FIG. 1. As discussed above, thetelematics devices 213 and 223 may receive vehicle operation data anddriving data from vehicle sensors 211 and 221, and may transmit the datato one or more external computer systems (e.g., driving analysis server250 of an insurance company, financial institution, or other entity)over a wireless transmission network. Telematics devices 213 and 223also may be configured to detect or determine additional types of datarelating to real-time driving and the condition of the vehicles 210 and220. In certain embodiments, the telematics devices 213 and 223 maycontain or may be integral with one or more of the vehicle sensors 211and 221 or system, such as an autonomous driving system. The telematicsdevices 213 and 223 also may store the type of their respective vehicles210 and 220, for example, the make, model, trim (or sub-model), year,and/or engine specifications, autonomous driving system specifications,as well as other information such as vehicle owner or driverinformation, insurance information, and financing information for thevehicles 210 and 220.

In the example shown in FIG. 2, telematics devices 213 and 223 mayreceive vehicle driving data from vehicle sensors 211 and 221, and maytransmit the data to a driving analysis server 250. However, in otherexamples, one or more of the vehicle sensors 211 and 221 or systems,including autonomous driving systems, may be configured to receive andtransmit data directly from or to a driving analysis server 250 withoutusing a telematics device. For instance, telematics devices 213 and 223may be configured to receive and transmit data from certain vehiclesensors 211 and 221 or systems, while other sensors or systems may beconfigured to directly receive and/or transmit data to a drivinganalysis server 250 without using the telematics devices 213 and 223.Thus, telematics devices 213 and 223 may be optional in certainembodiments.

In certain embodiments, vehicle sensors, vehicle OBD, autonomous drivingsystems, and/or vehicle communication systems, may collect and/ortransmit data pertaining to autonomous driving of the vehicles. Inautonomous driving, the vehicle fulfills all or part of the drivingwithout being piloted by a human. An autonomous car can be also referredto as a driverless car, self-driving car, or robot car. For example, inautonomous driving, a vehicle control computer 217, 227 may beconfigured to operate all or some aspects of the vehicle driving,including but not limited to acceleration, braking, steering, and/orroute navigation. A vehicle with an autonomous driving capability maysense its surroundings using the vehicle sensors 221, 221 and/or receiveinputs regarding control of the vehicle from the vehicle communicationssystems, including but not limited to short range communication systems212, 222, Telematics 213, 223, or other vehicle communication systems.

In certain embodiments, mobile computing devices 215 and 225 within thevehicles 210 and 220 may be used to collect vehicle driving data and/orto receive vehicle driving data from vehicle communication systems andthen to transmit the vehicle driving data to the driving analysis server250 and other external computing devices. Mobile computing devices 215and 225 may be, for example, mobile phones, personal digital assistants(PDAs), or tablet computers of the drivers or passengers of vehicles210, 220. Software applications executing on mobile devices 215, 225 maybe configured to detect certain driving data independently and/or maycommunicate with vehicle sensors 211, 221, Telematics 213, 223,autonomous driving systems, or other vehicle communication systems toreceive additional driving data. For example, mobile devices 215, 225equipped with GPS functionality may determine vehicle location, speed,direction and other basic driving data without needing to communicatewith the vehicle sensors 211 or 221, or any vehicle system. In otherexamples, software on the mobile devices 215, 225 may be configured toreceive some or all of the driving data collected by vehicle sensors211, 221. Mobile computing devices 215 and 225 may also be involved withaspects of autonomous driving, including receiving, collecting, andtransmitting vehicle operational data regarding autonomous driving andautonomous driving relationships between multiple vehicles.

When mobile computing devices 215 and 225 within the vehicles 210 and220 are used to detect vehicle driving data and/or to receive vehicledriving data from vehicles 211 and 221, the mobile computing devices 215and 225 may store, analyze, and/or transmit the vehicle driving data toone or more other devices. For example, mobile computing devices 215 and225 may transmit vehicle driving data directly to one or more drivinganalysis servers 250, and thus may be used in conjunction with orinstead of telematics devices 213 and 223. Additionally, mobilecomputing devices 215 and 225 may be configured to perform the V2Vcommunications described above, by establishing connections andtransmitting/receiving vehicle driving data to and from other nearbyvehicles. Thus, mobile computing devices 215 and 225 may be used inconjunction with, or instead of, short-range communication systems 212and 222 in some examples. In addition, mobile computing devices 215 and225 may be used in conjunction with the vehicle control computers 217and 227 for purposes of autonomous driving. Moreover, the processingcomponents of the mobile computing devices 215 and 225 may be used toanalyze vehicle driving data, determine a distance-based autonomousdriving insurance factor, determine properties related to aspects of avehicle insurance policy, and perform other related functions.Therefore, in certain embodiments, mobile computing devices 215 and 225may be used in conjunction with, or in place of, the driving analysiscomputers 214 and 224.

Vehicles 210 and 220 may include driving analysis computers 214 and 224,which may be separate computing devices or may be integrated into one ormore other components within the vehicles 210 and 220, such as theshort-range communication systems 212 and 222, telematics devices 213and 223, autonomous driving systems, or the internal computing systemsof vehicles 210 and 220. As discussed above, driving analysis computers214 and 224 also may be implemented by computing devices independentfrom the vehicles 210 and 220, such as mobile computing devices 215 and225 of the drivers or passengers, or one or more separate computersystems 230 (e.g., a user's home or office computer). In any of theseexamples, the driving analysis computers 214 and 224 may contain some orall of the hardware/software components as the computing device 101depicted in FIG. 1. Further, in certain implementations, thefunctionality of the driving analysis computers, such as storing andanalyzing vehicle driving data, determining a distance-based autonomousdriving insurance factor, and determining aspects of insurance policies,may be performed in a central driving analysis server 250 rather than byindividual vehicles 210 and 220. In such implementations, the vehicles210 and 220 might only collect and transmit vehicle driving data to adriving analysis server 250, and thus the vehicle-based driving analysiscomputers 214 and 224 may be optional.

Driving analysis computers 214 and 224 may be implemented in hardwareand/or software configured to receive vehicle driving data from vehiclesensors 211 and 221, short-range communication systems 212 and 222,telematics devices 213 and 223, vehicle control computer 217 and 227,autonomous driving systems, and/or other driving data sources. Vehiclesensors/OBDs 211 and 221, short-range communication systems 212 and 222,telematics devices 213 and 223, vehicle control computer 217 and 227,autonomous driving systems, and/or other driving data sources can bereferred to herein individually or collectively as a vehicle dataacquiring component. The driving analysis computer 214, 224 may comprisean electronic receiver to interface with the vehicle data acquiringcomponents to receive the collected data. After receiving, via theelectronic receiver, the vehicle driving data from, for example, avehicle data acquiring component, the driving analysis computers 214 and224 may perform a set of functions to analyze the driving data anddetermine properties related to vehicle insurance.

For example, the driving analysis computers 214 and 224 may include oneor more distance-based autonomous driving insurance factor algorithms,which may be executed by software running on generic or specializedhardware within the driving analysis computers. The driving analysiscomputer 214 in a first vehicle 210 may use the vehicle driving datareceived from that vehicle's sensors 211, along with vehicle drivingdata for other nearby vehicles received via the short-rangecommunication system 212, to determine a distance-based autonomousdriving insurance factor and determine properties related to vehicleinsurance applicable to the first vehicle 210 and the other nearbyvehicles. Within the driving analysis computer 214, a vehicle insuranceproperty function may use the results of the driving analysis performedby the computer 214 to determine/adjust a property of an insurancepolicy associated with the vehicle 210 and/or a driver of a vehicle 210.Further descriptions and examples of the algorithms, functions, andanalyses that may be executed by the driving analysis computers 214 and224 are described below, including in reference to FIGS. 3, 4, and 5.

As another example, the driving analysis computers 214 and 224 mayinclude one or more algorithms configured to generate vehicle insurancerates based on driver-independent variables and/or driver-dependentvariables, generate vehicle insurance rates based on changes in thelevel of autonomy of vehicles according to one or more aspects of thedisclosure, track vehicle density and/or generate vehicle insurancerates based on vehicle density, analyze use of autonomous vehiclefeatures and/or maintenance of autonomous vehicles, and enable vehicleteaching features and/or monitor the driver's response to teachingfeatures, as will be described in further detail in the examples below.

The system 200 also may include a driving analysis server 250,containing some or all of the hardware/software components as thecomputing device 101 depicted in FIG. 1. The driving analysis server 250may include hardware, software, and network components to receivevehicle operational data/driving data from one or more vehicles 210 and220, and other data sources. The driving analysis server 250 may includea driving data and driver data database 252 and driving analysiscomputer 251 to respectively store and analyze driving data receivedfrom vehicles and other data sources. The driving analysis server 250may initiate communication with and/or retrieve driving data fromvehicles 210 and 220 wirelessly via telematics devices 213 and 223,mobile devices 215 and 225, or by way of separate computing systems(e.g., computer 230) over one or more computer networks (e.g., theInternet). Additionally, the driving analysis server 250 may receiveadditional data from other non-vehicle data sources, such as externaltraffic databases containing traffic data (e.g., amounts of traffic,average driving speed, traffic speed distribution, and numbers and typesof accidents, etc.) at various times and locations, external weatherdatabases containing weather data (e.g., rain, snow, sleet, and hailamounts, temperatures, wind, road conditions, visibility, etc.) atvarious times and locations, and other external data sources containingdriving hazard data (e.g., road hazards, traffic accidents, downedtrees, power outages, road construction zones, school zones, and naturaldisasters, etc.), route and navigation information, and insurancecompany databases containing insurance data (e.g., driver score,coverage amount, deductible amount, premium amount, insured status) forthe vehicle, driver, and/or other nearby vehicles and drivers.

Data stored in the driving data database 252 may be organized in any ofseveral different manners. For example, a table in database 252 maycontain all of the vehicle operation data for a specific vehicle 210,similar to a vehicle event log. Other tables in the database 252 maystore certain types of data for multiple vehicles. For instance, tablesmay store specific data sets, including data types discussed above (e.g.road-type information, insurance data, etc.).

The driving analysis computer 251 within the driving analysis server 250may be configured to retrieve data from the database 252, or may receivedriving data directly from vehicles 210 and 220 or other data sources,and may perform driving data analyses, determine distance-basedautonomous driving insurance factor, and/or vehicle insurancedeterminations, and other related functions. The functions performed bythe driving analysis computer 251 may be similar to those of drivinganalysis computers 214 and 224, and further descriptions and examples ofthe algorithms, functions, and analyses that may be executed by thedriving analysis computer 251 are described below, including inreference to FIGS. 3 through 10.

In various examples, the driving data analyses, mileage unitdeterminations, and/or insurance property determinations may beperformed entirely in the driving analysis computer 251 of the drivinganalysis server 250 (in which case driving analysis computers 214 and224 need not be implemented in vehicles 210 and 220), or may beperformed entirely in the vehicle-based driving analysis computers 214and 224 (in which case the driving analysis computer 251 and/or thedriving analysis server 250 need not be implemented). In other examples,certain driving data analyses may be performed by vehicle-based drivinganalysis computers 214 and 224, while other driving data analyses areperformed by the driving analysis computer 251 at the driving analysisserver 250. For example, a vehicle-based driving analysis computer 214may continuously receive and analyze driving data from nearby vehiclesto determine certain driving characteristics (e.g., mileage units ofdistance traveled by the vehicle when the vehicle is engaged inautonomous driving or other data as described herein) so that largeamounts of driving data need not be transmitted to the driving analysisserver 250. However, for example, after a mileage unit is determined bythe vehicle-based driving analysis computer 214, the information may betransmitted to the server 250, and the driving analysis computer 251 maydetermine if a property of the insurance policy should be updated.

FIG. 3 and FIG. 4 are flow diagrams illustrating example methods ofdetermining a property of an insurance policy based on analysis ofvehicle operational data of vehicles engaged in autonomous driving. FIG.3 includes an example step of determining a distance-based autonomousdriving insurance rating factor. The examples of FIG. 3 and FIG. 4 maybe performed by one or more computing devices in a driving analysissystem, such as vehicle-based driving analysis computers 214 and 224, adriving analysis computer 251 of a driving analysis server 250, usermobile computing devices 215 and 225, and/or other computer systems.

The steps shown in FIG. 3 describe performing an analysis of vehicleoperational data to determine a distance-based autonomous drivinginsurance rating factor of vehicles engaged in an autonomous driving anddetermining a property of an insurance policy based on the factor. Instep 301, vehicle operational data may be received from a first vehicle210. As described above, a driving analysis computer 214 may receive andstore vehicle driving data from a vehicle data acquiring component,including but not limited to the vehicle's internal computer systems andany combination of the vehicle's sensors/OBD 211 and/or communicationsystems. The data received in step 301 may include, for example, anidentifier that the vehicle is engaged in autonomous driving. The datareceived in step 301 may include, for example, the location, speed,direction of travel, distance traveled, distance traveled while engagedin autonomous driving, object proximity data from the vehicle's externalcameras and proximity sensors, and data from the vehicle's varioussystems used to determine if the vehicle 210 is braking, accelerating,or turning, etc., and status of the vehicle's user-operated controls(e.g., head lights, turn signals, hazard lights, radio, phone, etc.),along with any other data collected by vehicle sensors/OBD 211 or datareceived from a nearby vehicle.

In step 302, the vehicle operational data is analyzed to determine oneor more mileage units. As used herein, a mileage unit indicates adistance traveled by the vehicle when the vehicle was engaged inautonomous driving for at least a portion of the distance traveled. Forexample, in an embodiment, a first mileage unit can indicate a totaldistance traveled by the vehicle when the vehicle was engaged inautonomous driving over a period of time. In addition, for example, asecond mileage unit can indicate a total distance traveled by thevehicle over the same period of time as the first mileage unit. In suchexamples, the first mileage unit would be equal to the second mileageunit if the vehicle was engaged in autonomous driving over the entiredistance driven over period of time. Or, the first mileage unit would beless than the second mileage unit if the vehicle was engaged inautonomous driving for only a portion of the distance driven over theperiod of time.

The driving analysis computer may determine a mileage unit from thevehicle operational data in multiple ways. The manner in which a mileageunit is determined may depend on the type of information included in thedriving data. For example, the driving data may include an identifierwhich indicates that the vehicle is engaged in autonomous driving andinformation indicating distance traveled by the vehicle. Informationindicating distance traveled may be obtained from, for example, thevehicle odometer, trip meter, and/or other distance measuring device ofthe vehicle. In addition, distance traveled information can bedetermined from other driving data including, for example, time andspeed information and/or location information, such as GPS. Examplealgorithms using time marked driving data are included in USPublications Number 2013/0073112 which is hereby incorporated byreference herein in its entirety. In addition, mileage units can bedetermined to indicate a distance traveled by the vehicle over a singletrip, multiple trips, a period of time, and/or in an ongoing tally.Mileage units can also be determined from, for example, contiguous ornon-contiguous distances traveled by the vehicle. Mileage units can alsobe determined from, for example, distances traveled when at least oneother condition is satisfied during travel, including a distancetraveled over a certain road-type, driving during a certain weathercondition, and/or driving in a certain location. A period of time canbe, for example, a six-month term of an insurance policy associated withthe vehicle. In addition, for example, a period of time can be a month,a week, a day, a hour, a second, and/or multiples or combinations of thesame.

In an example, the driving analysis computer can determine a mileageunit using an autonomous driving identifier to determine when thevehicle was engaged in autonomous driving and the distance traveledinformation collected from a distance measuring device of the vehicle todetermine any number of various distances traveled by the vehicle whenthe vehicle was engaged in autonomous driving for at least a portion ofthe distance traveled. In an example, a mileage unit indicating a totaldistance traveled by the vehicle when the vehicle was engaged inautonomous driving over a sixth month period of time can be determinedby adding all the distance segments traveled within the six-month timeperiod when the autonomous driving indicator indicates that the vehiclewas engaged in autonomous driving.

In certain embodiments, a mileage unit can be determined based ondriving data additional to a distance traveled by the vehicle when thevehicle was engaged in autonomous driving for at least a portion of thedistance traveled. For example, such additional driving data caninclude, for example, period of time, a road-type (e.g. highway, sideroad, etc.), road condition, speed, driver data, weather condition,time-of-day, driving event or action, congestion level, location, etc.For example, a first mileage unit can be determined to indicate a totaldistance traveled by the vehicle over a first road-type when the vehiclewas engaged in autonomous driving for at least a portion the distanceand a second mileage unit can be determined to indicate a total distancetraveled by the vehicle over the first road-type when the vehicle wasengaged in autonomous driving. In such example, the first mileage unitindicates total distance traveled over the road-type, and the secondmileage unit indicates the amount of such total for which the vehiclewas engaged in autonomous driving.

In step 303, the mileage units determined in step 302 may be used todetermine a distance-based autonomous driving insurance rating factor.In addition, in an example, in step 303, the mileage units determined instep 302 and additional driving data may be used to determine adistance-based autonomous driving insurance factor. In an embodiment, amileage unit determined in step 302 and additional driving data areinput variables used to determine a distance-based autonomous drivinginsurance factor. For example, in step 303, the mileage units determinedin step 302 and an autonomous driving system quality rating may be usedto determine a distance-based autonomous driving insurance factor. Inanother example, in step 303, the mileage units determined in step 302and an autonomous driving characteristic or event determined from thedriving data can be used to determine a distance-based autonomousdriving insurance factor. An autonomous driving characteristic orautonomous driving event can include, for example, actions performed orevents undertaken by the vehicle or nearby vehicles—such as that thevehicle was a lead vehicle in an autonomous droning relationship or thatthe vehicle engaged in self-parking.

In step 304, a property of an insurance policy may be determined usingthe distance-based autonomous driving insurance rating factor. Theproperty of an insurance policy can include any of a number of aspectsof a vehicle insurance policy. For example, a property of an insurancepolicy can include a premium, a deductible, a coverage term, a coverageamount, or other attribute of an insurance policy. In variousembodiments, the property can be determined in accordance with rules setforth by the insurance provider. For example, the property of thevehicle insurance policy may change depending upon any number of drivingdata points, driver information, and other information. For example, instep 304, a distance-based autonomous driving insurance factor may bedetermined using one or more mileage units determined in step 303. Forexample, in step 304, a distance-based autonomous driving insurancefactor may be determined using a comparison between one or more mileageunits. In an example, in step 304 a comparison between two or mileageunits can be a ratio. For example, a ratio can be between a firstmileage unit that indicates a total distance traveled by the vehiclewhen the vehicle was engaged in autonomous driving over the first periodof time and a second mileage unit that indicates a total distancetraveled by the vehicle over the first period of time. In such example,the ratio can indicate a percentage of the total distance traveled bythe vehicle over the first period of time where the vehicle was engagedin autonomous driving. The ratio can be used to determine a property ofa vehicle insurance policy. In an example, in step 304, where theproperty of the vehicle insurance policy is a premium, a first premiumrate can be applied when the ratio is above a threshold value and asecond premium rate can be applied when the ratio is below the thresholdvalue. In an example, one or more premium rates can be applied on aper-mile basis.

In step 305, the driving analysis computer can adjust or cause to adjustthe insurance policy based on the determined property. In variousembodiments, the adjustment can occur during the coverage term and/orprior to an initial or subsequent coverage term. In addition, the policyholder may be notified of the adjustment. Alternatively, the adjustmentcan come in the form of a reward. Examples of using driving data todetermine rewards, including driver rewards related to vehicleinsurance, are disclosed in U.S. application Ser. No. 14/163,741 whichis hereby incorporated by reference herein in its entirety.

Referring to FIG. 4, the steps shown in FIG. 4 describe an example ofperforming an analysis of vehicle operational data to determine mileageunits indicating distances traveled by a vehicle over a period of timewhen the vehicle was engaged in autonomous driving over at least aportion of the distance traveled. In step 401, vehicle operational datamay be received from a first vehicle 201. In step 402, the vehicleoperational data is analyzed and a first mileage unit is determined toindicate a total distance traveled by the vehicle when the vehicle wasengaged in autonomous driving over a period of time. In step 403, thevehicle operational data is analyzed and a second mileage unit isdetermined to indicate a total distance traveled by the vehicle over thesame period of time as for the first mileage unit. In step 404, apremium of an insurance policy associated with the vehicle is determinedusing a ratio of the first mileage unit and the second mileage unit. Instep 405, a first premium rate is applied when the ratio is above athreshold value and a second premium rate is applied when the ratio isbelow the threshold value.

Referring to FIG. 5, an autonomous driving insurance rating factorcalculator 502 may calculate the distance-based autonomous drivinginsurance rating factor using at least one input variable. In variousembodiments, an input variable can include at least one mileage unit orat least one mileage unit and additional driving data. The additionaldriving data may include, but is not limited to, at least one of:vehicle speed, location, road-type, weather condition, driver score,vehicle's characteristics (e.g., vehicle type-SUV, sports car, sedan,convertible, etc., vehicle's turning radius, vehicle's maximum speed,vehicle time to accelerate from 0-60 mph, and other characteristics tiedto the specific vehicle), driving risk characteristics/profile of thedriver/operator, and other characteristics.

In an embodiment, an input variable can include an autonomous drivingsystem quality rating. For example, assuming numerous systems exist forautonomous driving, “System A” may use hardware and/or softwarealgorithms different from that of competing “System B.” As a result,each of the systems may react differently when used in the real world,and as such, will earn a driving risk characteristic/profilecommensurate with the amount of risk associated with the particularsystem. In an embodiment, an autonomous driving system quality ratingmay indicate a rating of the likelihood of an autonomous driving systemof the vehicle to avoid accidents involving the vehicle. Therefore, anautonomous driving insurance rating factor calculator 402 may take intoaccount different quality rating/level of risk for “System A” than for“System B,” in some examples. In another example, the autonomous drivingsystem quality rating may take into account factors such as number ofaccidents, moving violations, number of submitted insurance claims, andother factors known for a particular autonomous driving system.

In addition, referring to FIG. 4, other information may also be inputtedinto the autonomous driving rating factor calculator 402 forconsideration in calculating a distance-based autonomous drivinginsurance rating factor or other autonomous driving insurance ratingfactor. For example, the congestion level (e.g., traffic) on a roadway,the weather conditions the roadway, historical occurrences of incidents(e.g., vehicular accidents) on the roadway, and other factors related tothe environment/surroundings in which the vehicle is operated. Forexample, the autonomous driving insurance rating factor calculator 402may adjust the factor based on the congestion level on the roadway beinghigh. In one example, the autonomous driving insurance rating factorcalculator 402 may determine a factor value which indicates elevatedrisk during rush hour traffic to encourage vehicles 402 equipped with anautonomous driving system to engage in autonomous driving. Congestionlevels may be divided, in one example, into categories of high, medium,and low based on the whether the travel time through a particularroadway falls into the upper ⅓, middle ⅓, or lower ⅓ of possible travelstimes historically logged on that roadway. Likewise, weather conditionsmay play a role in determining risk level. For example, in a fogsituation, the risk may be relatively higher for manual driving versusautonomous driving. In order to encourage the driver to engage inautonomous driving, the calculator may determine a factor whichindicates elevated risk for manual driving and lower risk for autonomousdriving. The driving analysis computing device can, for example,determine a deductible amount which is higher for manual driving in thefog than autonomous driving in the fog. The driving analysis computingdevice can notify the driver of the vehicle of the deductible amountand/or difference in deductible amount to encourage the driver to engagein autonomous driving. The notice can be delivered in real-time to, forexample, a display system of the vehicle or user device, such as mobilephone of the driver.

The various data from the preceding examples may be stored at andretrieved from various data sources, such as an external trafficdatabases containing traffic data (e.g., amounts of traffic, averagedriving speed, traffic speed distribution, and numbers and types ofaccidents, etc.) about various times and locations, external weatherdatabases containing weather data (e.g., rain, snow, sleet, and hailamounts, temperatures, wind, road conditions, visibility, etc.) atvarious times and locations, and other external data sources containingdriving hazard data (e.g., road hazards, traffic accidents, downedtrees, power outages, road construction zones, school zones, and naturaldisasters, etc.), and insurance company databases containing insurancedata (e.g., driver score, coverage amount, deductible amount, premiumamount, insured status) for the vehicle, driver, and/or other nearbyvehicles and drivers. The data may, in some examples, be wirelesslytransmitted from a remote server and/or database to the vehicle 220 forconsideration by the autonomous driving insurance rating factorcalculator 402. As explained earlier, vehicles 210 may leverageadditional hardware and/or software capabilities of another vehicle 220or vehicles to gain access to the driving data and other information,when desired. For example, a vehicle 220 may receive, through itslong-range communications circuitry 222 (or mobile phone 225), drivingdata/information and forward it to vehicles 210 via their short-rangecommunications 212 systems. As such, the vehicles 210, 220 may input theinformation into their autonomous driving insurance rating factorcalculator 402 for consideration.

FIG. 5 shows the autonomous driving insurance rating factor calculator502 receiving numerous inputs and outputting a distance-based autonomousdriving insurance rating factor. In some examples, the autonomousdriving insurance rating factor calculator 502 may be anapplication-specific integrated circuit (ASIC) designed to perform thefunctionality described herein. In other examples, the autonomousdriving insurance rating factor calculator 502 may use a processing unit(e.g., comprising a computer processor, such as an Intel™ x86microprocessor or other special-purpose processors) andcomputer-executable instructions stored in a memory to cause a drivinganalysis computer 214 to perform the steps described herein.

As shown in FIG. 2, a single vehicle-based driving analysis computer 214may receive driving data for a first vehicle 210 (steps 301, 401),including driving data received from V2V communications includingdriving data for one or more other vehicles, may determine from the datawhether the vehicle is engaged in an autonomous driving, and maydetermine a characteristic of the autonomous driving (step 302, 402, and403), determine a property of an insurance policy based on thecharacteristic (step 304, 404), and adjust the insurance policy based onthe determined property (step 305). However, other driving analysiscomputers and/or other computing devices may be used to some or all ofthe steps and functionality described above in reference to FIGS. 3, 4,and 5. For example, any of steps 301-305, 401-405 may be performed by auser's mobile device 215 or 225 within the vehicles 210 or 220. Thesemobile devices 215 or 225, or another computing device 230, may executesoftware configured to perform similar functionality in place of thedriving analysis computers 214 and 224. Additionally, some or all of thedriving analysis functionality described in reference to FIGS. 3, 4, and5 may be performed by a driving analysis computer 251 at a non-vehiclebased driving analysis server 250. For example, vehicles 210 and 220 maybe configured to transmit their own vehicle sensor data, and/or the V2Vcommunications data received from other nearby vehicles, to a centraldriving analysis server 250 via telematics devices 213 and 223.

While systems already exist for autonomous vehicles, such as theself-driving car by GOOGLE™, the spirit of this disclosure is notlimited to just autonomous self-driving cars. For example, the vehicle220 may be a completely autonomous vehicle, semi-autonomous vehicle, ora manual human-driven vehicle. As used herein, the term autonomous(e.g., autonomous vehicle) may refer to either semi-autonomous (e.g.,semi-autonomous vehicle) or completely autonomous (e.g., completelyautonomous vehicle). Depending on the capabilities of the vehicle 220,the vehicle may be equipped with the appropriate sensors 221 and otherelectronic components to enable the automation/semi-automation, as isalready known in the relevant art of autonomous/semi-autonomousvehicles. Similarly, an autonomous drone vehicle may be equipped withthe appropriate hardware and software to operate as an autonomousvehicle, semi-autonomous vehicle, or a manually-driven vehicle. Incontrast, however, in some examples, an autonomous drone vehicle may beequipped with less hardware and/or software than a vehicle with completeautonomous capability because to some extent, the a drone vehicle mayrely upon the lead vehicle to provide guidance and commands forcontrolling the speed, acceleration, braking, cornering, route, andother operation of the following vehicle. For example, a following dronevehicle may transmit data to the lead vehicle using its short-rangewireless communications system, and rely upon long-range wirelesscommunication capabilities of the lead vehicle to forward the data tothe appropriate final destination. At least one benefit of such anarrangement is that the cost/price of a following drone vehicle may beless than that of other vehicles (e.g., lead vehicle) due to reducedcomplexity and reduce hardware and/or software requirements. In anembodiment, an autonomous driving system quality rating takes intoaccount whether a vehicle is equipped for autonomous droning.

In addition, the integrity of collected vehicle driving data may bevalidated by comparing, e.g., by a driving analysis computer, thedriving data (e.g., location, speed, direction) from one vehicle'ssensors 211 with corresponding driving data from a nearby vehicle 220.In one example, driving data of the nearby vehicle can be collected by adata acquiring component of a following/drone vehicle 210 via, forexample, vehicle V2V. In one example, the driving data of the nearbyvehicle may be directly received from the nearby vehicle.

FIG. 6 is a flow diagram illustrating an example method of generatingvehicle insurance rates based on driver-independent variables and/ordriver-dependent variables according to one or more aspects of thedisclosure. The steps illustrated in FIG. 6 may be performed by one ormore computing device 101. For example, a vehicle computing device(e.g., driving analysis computer 214 or vehicle control computer 217)and/or a driving analysis computing device (e.g., driving analysiscomputer 251) may perform one or more of the steps illustrated in FIG.6.

In step 606, a computing device may determine whether a request todetermine a property of an insurance policy, such as an insurance quotefor a vehicle, has been received. If a request has not been received(step 606: N), the computing device may wait until a request has beenreceived. Alternatively, the computing device might not wait for arequest before determining the property of the insurance policy.

In step 608, the computing device may determine whether the vehicle tobe quoted is completely autonomous. If the vehicle is not completelyautonomous, the computing device may determine whether the vehicle issemi-autonomous in step 614. The computing device may make each of thesedeterminations by comparing the features of the vehicle with thefeatures included in an exemplary (or standard) completely autonomousvehicle or an exemplary (or standard) semi-autonomous vehicle. Thefeatures for exemplary vehicles may be stored in, for example, adatabase, such as database 252.

Completely autonomous vehicles may have more autonomous features thansemi-autonomous vehicles. For example, the standard completelyautonomous vehicle may have a first group of features, such asautonomous speed control, autonomous steering, autonomous braking, andautonomous parallel parking. The standard semi-autonomous vehicle mayhave a second group of features, which may be a subset of the firstgroup of features. For example, the standard semi-autonomous vehicle mayhave autonomous braking and autonomous parallel parking. Theseclassifications may be stored in a database.

If the vehicle to be quoted has all of the features of the standardcompletely autonomous vehicle (e.g., based on a comparison of thefeatures of the vehicle to be quoted and the features of the standardcompletely autonomous vehicle), the computing device may determine thatthe vehicle to be quoted is a completely autonomous vehicle (step 608:Y). If the vehicle to be quoted has all of the features of the standardsemi-autonomous vehicle, the computing device may determine that thevehicle is a semi-autonomous vehicle (step 614: Y). If the vehicle doesnot have all the features of the standard semi-autonomous vehicle (step614: N), the computing device may determine that the vehicle is anon-autonomous or standard vehicle.

While three different classifications have been described (e.g.,completely autonomous, semi-autonomous, and non-autonomous), any numberof classifications may exist. For example, five differentclassifications may be used: (1) assisted, (2) partial automation, (3)conditional automation, (4) high automation, and (5) full automation. Inassisted mode, the vehicle's computer-operated systems may assist inemergency situations. The system takes over either steering oracceleration in specific modes using information about the drivingenvironment. The driver may do everything else. Exemplary autonomousfeatures in the assisted mode may include lane keeping automation,cruise control, electronic stability control, and automatic braking.

In partial automation mode, the automation system may take control ofsteering and acceleration in specific driving modes using informationabout the driving environment. The driver may do everything else. Thismode may be beneficial in low speed environments, if there are no (orvery few) intersections, and the driver is alert. Exemplary autonomousfeatures in the partial automation mode may include traffic jam assistand adaptive cruise control, in addition to or instead of the autonomousfeatures in the assisted mode.

In conditional automation, the system may perform all (or most) aspectsof the dynamic driving task in specific driving modes. The driver may beavailable to respond to a request by the autonomous system to intervene.For example, the driver may be present in driver's seat but would nothave to stay alert to the driving environment. Exemplary autonomousfeatures in the conditional automation mode may include a traffic-jamautopilot system, in addition to of instead of the autonomous featuresin the partial automation mode.

In high automation mode, the system may perform all aspects of thedynamic driving task in specific driving modes, even if the human driverdoes not respond appropriately to a request to intervene. For example, afull freeway autopilot system may be used. The driver, in somecircumstances, may input a desired destination but might not be expectedto take an active role in driving the vehicle. Exemplary autonomousfeatures in the high automation mode may include a full freewayautopilot system, in addition to or instead of the autonomous featuresin the partial automation mode.

In full automation mode, the system may perform all aspects of thedynamic driving task in all driving modes under all roadway conditions.Vehicles running in this mode may include truly driverless cars, such asrobotic taxis. Exemplary autonomous features in the high automation modemay include autonomous features in addition to or instead of theautonomous features in the high automation mode.

Any number of levels of vehicle automation may be used, including thosedefined by standards-setting organizations, such as the National HighwayTraffic Safety Administration (NHTSA) and SAE International. Forexample, the definitions available in the NHTSA's “Preliminary Statementof Policy Concerning Automated Vehicles” may be used, the entirety ofwhich is hereby incorporated by reference. “Preliminary Statement ofPolicy Concerning Automated Vehicles,” NHTSA, May 30, 2013, available athttp://www.nhtsa.gov/staticfiles/rulemaking/pdf/Automated_Vehicles_Policy.pdf.As another example, the definitions available in SAE International's“Summary of Levels of Driving Automation for On-road Vehicles,” may beused, the entirety of which is hereby incorporated by reference. BryantWalker Smith, “Summary of Levels of Driving Automation for On-roadVehicles,” Center for Internet and Society, Stanford Law School, Dec.18, 2013, available athttp://cyberlaw.stanford.edu/blog/2013/12/sae-levels-driving-automation.As will be described in further detail in the examples below, theclassification of the vehicle may be used to determine the types ofvariables (e.g., driver-independent variables or driver-dependentvariables) considered in generating an insurance quote or other propertyof an insurance policy for the vehicle.

The vehicle classification may change between completely autonomous,semi-autonomous, and non-autonomous over time. In some aspects, a changein the classification system may prompt a change in the vehicleclassification. For example, at a first time, a vehicle may beclassified as completely autonomous if it has feature A, feature B,feature C, and feature D. At a second time later than the first time,the classification system may change the definition of a completelyautonomous vehicle to a vehicle that has features A-D along with featureE. If the quoted vehicle has features A-D, but not feature E, it may beclassified as a completely autonomous vehicle at the first time.However, the classification may change from completely autonomous tosemi-autonomous in the future (e.g., at the second time) based on thechange in the classification system.

The vehicle's classification may also change based on a change in thevehicle's autonomous features. For example, assume that a vehicle isclassified as completely autonomous if it has features A-D. The vehiclemay have been classified as a semi-autonomous if, in the past, thevehicle had features A-C. However, the vehicle may be updated withfeature D (e.g., an automated steering feature) at a later time. Thecomputing device may re-classify the vehicle as completely autonomous atthe later time.

If the vehicle is completely autonomous (step 608: Y), the computingdevice, in step 610, may determine one or more driver-independentvariable used to generate an insurance quote for the vehicle. Thedriver-independent variable may comprise a vehicle identifier, such as aVehicle Identification Number (VIN). The computing device may obtain thevehicle identifier from the driver or retrieve it from a database, suchas a driver motor vehicle (DMV) database, a database of the insuranceprovider if the driver previously provided the vehicle identifier, orany other database. Using the vehicle identifier, the computing devicemay determine additional data associated with the vehicle identifier,such as the make, year, and model of the vehicle, the autonomous orsafety features of the vehicle, or any other information for thevehicle. The computing device may provide the vehicle identifier to adatabase or search engine, such as a VIN system, and the database orsearch engine may provide the computing device with the additionalinformation.

In some aspects, the vehicle information in a VIN system might not be upto date. For example, a newer vehicle, such as a vehicle released withinthe last year, might have new safety and/or autonomous features that arenot currently associated with the VIN for the vehicle. A user, such asan agent or administrator, may update the VIN system by manually addingnew vehicle information to the VIN system or updating existinginformation in the system. For example, the user may input, through acomputing device, the autonomous features for a particular type ofvehicle or for a particular VIN into the VIN system. Accordingly, theautonomous features for each VIN in the VIN system may be kept up todate.

In step 612, the computing device may generate an insurance quote orother property of an insurance policy based on one or more of thedriver-independent variables determined in step 610. As previouslydiscussed, the property of the insurance policy may comprise a premium,a deductible, a coverage term, a coverage amount, or any other insuranceproperty. In some aspects, the computing device may generate a quotebased on the identifier of the vehicle, and not based on anydriver-dependent variables. In other words, the computing device maygenerate a quote and/or bind based on the vehicle identifier alone andnot based on personal information of the driver (e.g., age, creditscore, driving history, etc.) because the driver's characteristics mightnot matter if the vehicle is completely autonomous.

The vehicle's classification may change from semi-autonomous tocompletely autonomous, as previously discussed. Correspondingly, thecomputing device may change the insurance quote to be based ondriver-independent variables and not based on driver-dependentvariables. As vehicles become more and more autonomous, fewerdriver-dependent rating variables may be introduced and a number of themmay be eliminated or rolled off. Greater reliance may be placed ondriver-independent variables, such as the VIN for quotes, binds, and/ordiscounts.

If the vehicle to be quoted is not completely autonomous (step 608: N)but is semi-autonomous (step 614: Y), the computing device, in step 616,may determine one or more driver-independent variable for generating aquote, as previously discussed with reference to step 610. Because thevehicle is semi-autonomous, characteristics of the driver may berelevant, and the computing device may also rely on driver-dependentvariables to generate the quote. Weights may be used for thedriver-independent variables and/or driver-dependent variables toemphasize some variables over other variables.

In step 618, the computing device may determine one or more weights forthe driver-independent variables determined in step 616. In step 620,the computing device may determine one or more driver-dependent variableused to generate the quote. Exemplary driver-dependent variablesinclude, but are not limited to, the age of the driver, a geographiclocation of the driver (e.g., the location of the driver's residence),driving record, credit score, whether the driver owns a home, and thelike. In step 622, the computing device may determine one or moreweights for the driver-dependent variables.

In step 624, the computing device may generate an insurance quote (orother property of an insurance policy) based on the variables andweights. Several non-limiting examples of this determination will now beprovided. For these examples, the following exemplary driver-independentvariables will be used: vehicle model and vehicle year. The followingexemplary driver-dependent variables will also be used: driver's age anddriver's residence.

The computing device may determine a value for each of the variablesconsidered. In some aspects, the values may be on the same scale, suchas a 1-10 scale. The computing device may determine the values bycomparing a characteristic of the driver or of the vehicle with adatabase that correlates characteristics with values. For example, thecomputing device may assign a value of 4 for the vehicle model variableif the vehicle is a small 2-door automobile, and the database correlatessmall 2-door automobiles with the value of 4. Similarly, the computingdevice may assign a value of 9 for the vehicle year variable if thevehicle was manufactured in 2013, and the database correlates 2013vehicles with the value of 9. The computing device may assign a value of3 for the driver's age variable if the driver is 20 years old, and thedatabase correlates drivers between the ages of 18 and 24 with the valueof 3. Finally, the computing device may assign the value of 8 for thedriver's residence if the driver lives in Iowa, and the databasecorrelates drivers residing in Iowa with the value of 8.

The computing device may also determine a weight for each variable(vehicle model, vehicle year, driver's age, and driver's residence) orfor each type of variable (driver-independent or driver-dependent). Insome aspects, the weights may be used to emphasize one or more variablesover one or more other variables. For example, a weight of 0.20 may beapplied to the vehicle model variable, a weight of 0.40 may be appliedto the vehicle year, a weight of 0.10 may be applied to the driver'sage, and a weight of 0.30 may be applied to the driver's residence. Inthis example, the vehicle may be given a score of 7.10 (e.g.,0.20*4+0.40*9+0.10*3+0.30*8=7.10). The score and/or the weights andvariables determined in steps 616, 618, 620, and 622 may be used by thecomputing device to generate the quote in step 624.

In some aspects, weights for the same types of variables (e.g.,driver-independent or driver-dependent) may be the same. For example,driver-independent variables may each be assigned the weight of 0.20.Driver-dependent variables may each be assigned the weight of 0.30. Inthis example, the vehicle may be given a score of 5.90 (e.g.,0.20*4+0.20*9+0.30*3+0.30*8=5.90).

The weights may depend on the level of autonomy of the vehicle. The moreautonomous the vehicle, the greater the weights for thedriver-independent variables and the smaller the weights for thedriver-dependent variables. If the vehicle changes from semi-autonomousto completely autonomous, the weights for the driver-dependent variablesmay drop to 0 (or drop to near zero) so that driver-dependent variablesare not considered (or are at least weighted less) when determining aninsurance quote for the vehicle. Determining the level of autonomy willbe described in further detail below in reference to FIG. 7.

In step 626, the computing device may generate a standard quote if thevehicle is a non-autonomous vehicle or otherwise does not qualify ascompletely autonomous or semi-autonomous.

FIG. 7 is a flow diagram illustrating an example method of generatingvehicle insurance rates based on changes in the level of autonomy ofvehicles according to one or more aspects of the disclosure. The stepsillustrated in FIG. 7 may be performed by one or more computing device101. For example, a vehicle computing device (e.g., driving analysiscomputer 214 or vehicle control computer 217) and/or a driving analysiscomputing device (e.g., driving analysis computer 251) may perform oneor more of the steps illustrated in FIG. 7.

In step 702, the computing device may optionally determine whether anevent has occurred, such as receiving a request for a vehicle re-quoteand/or determining that the level of autonomy of the insured vehicle haschanged since the time of the original quote or bind. For example, thecomputing device may automatically detect a change in the level ofautonomy of the vehicle, and generate a quote in response to thedetection. The vehicle may send data identifying its level of autonomyto the computing device in response to a change in the level of autonomyor periodically (e.g., weekly, monthly, etc.). Additionally oralternatively, the computing device may retrieve information identifyinga vehicle's level of autonomy from a database, such as a third partydatabase. The database may be updated with level of autonomy informationfor a plurality vehicles or vehicle types (e.g., make, model, year,etc.). The computing device may retrieve the correct information byproviding the database with the VIN, model and year, or any otherinformation identifying the vehicle. If an event has not occurred (step702: N), the computing device may optionally wait for an event to occur.

In step 704, the computing device may determine the level of autonomy ofthe vehicle to quote. The level of autonomy may be based on the overallclassification of the vehicle (e.g., completely autonomous,semi-autonomous, or non-autonomous), as previously discussed. The levelof autonomy may additionally or alternatively be based on eachautonomous feature of the vehicle (e.g., automatic speed control,automatic steering, automatic braking, automatic parallel parking, andthe like). For example, the computing device may count the number ofautonomous features to determine the level of autonomy. The computingdevice may also apply weights to each of the autonomous features todetermine the level of autonomy. For example, automatic steering may beemphasized more than automatic braking, and automatic braking may beemphasized more than automatic parallel parking.

The computing device may determine whether a level of autonomy of thevehicle has changed, such as from the last time a quote or bind wasgenerated for the vehicle. For example, in step 706, the computingdevice may determine whether the level of autonomy increased. If thelevel of autonomy has not increased (step 706: N), the computing devicemay determine whether the level of autonomy has decreased in step 714.If the level of autonomy has not changed, the computing device maymaintain the original quote or bind in step 720.

The computing device may determine that the level of autonomy of thevehicle has changed if the classification of the vehicle has changed,which (as previously discussed) may have been caused by a change in theclassification system or a change in the autonomous features of thevehicle. The computing device may additionally or alternatively detect achange in the level of autonomy based on a modification, addition, orremoval of an automated feature. For example, if the software orhardware for an autonomous feature, such as autonomous braking, has beenupgraded, the computing device may determine that the level of autonomyincreased (step 706: Y). Alternatively, the computing device maydetermine that the level of autonomy decreased if an autonomous featureis removed and/or has been disabled (step 714: Y).

In response to a determination that the level of autonomy of the vehiclehas changed, the computing device may adjust (e.g., increase ordecrease) the weights for one or more of the variables. For example, thecomputing device may increase a weight for a driver-independent variablein step 708 and/or decrease a weight for a driver-dependent variable instep 710 if the level of autonomy has increased (step 706: Y). On theother hand, the computing device may decrease a weight for thedriver-independent variable in step 716 and/or increase a weight for thedriver-dependent variable in step 718 if the level of autonomy hasdecreased (step 714: Y).

In step 712, the computing device may generate a quote (or otherproperty of the insurance policy) using the new weights. For example, ifthe vehicle is now more autonomous, the computing device may determinethe quote based on at least one of an increased weight for adriver-independent variable of the vehicle and a decreased weight for adriver-dependent variable of the vehicle. The weights for thedriver-dependent variables may eventually drop to 0, and a quote may bebased on one or more driver-independent variables, but not based ondriver-dependent variables. This may occur, for example, if the vehiclebecomes classified as completely autonomous. If, on the other hand, thevehicle is now less autonomous, the computing device may determine thequote based on at least one of a decreased weight for adriver-independent variable of the vehicle and an increased weight for adriver-dependent variable of the vehicle.

In some aspects, the level of autonomy may change multiple times, suchas during one or more of the driver's trips with the vehicle. Thecomputing device may determine an average or median level of autonomyand generate a value for a property of the insurance policy based on theaverage or median level of autonomy. Additionally or alternatively, thevalue for the property of the insurance policy may vary over time. Forexample, the computing device may determine that the vehicle was used ata first level of autonomy over a first period of time (e.g., April).During a second period of time after the first (e.g., May), thecomputing device may determine that the vehicle was used at a secondlevel of autonomy greater than the first level of autonomy. Accordingly,the computing device may vary, such as decrease, the value for theproperty of the insurance policy (e.g., deductible, premium, and thelike) for the second period of time relative to the value for the firstperiod of time. Thus, variable rate insurance may be generated based onthe level of autonomy of the vehicle over predetermined periods of time(e.g., each month, every 3 months, and the like). The vehicle, driver,or owner may be notified of the decrease or increase in the value of theinsurance policy, which may encourage the driver or owner of the vehicleto use more autonomous features to decrease the amount of the deductibleor premium.

FIG. 8 is a flow diagram illustrating an example method of trackingvehicle density and/or generating vehicle insurance rates based onvehicle density according to one or more aspects of the disclosure. Thesteps illustrated in FIG. 8 may be performed by one or more computingdevice 101. For example, a vehicle computing device (e.g., drivinganalysis computer 214 or vehicle control computer 217) and/or a drivinganalysis computing device (e.g., driving analysis computer 251) mayperform one or more of the steps illustrated in FIG. 8.

In step 802, a computing device may monitor the density (e.g.,congestion) of vehicles around an insured vehicle or vehicle to bequoted (also referred to as a target vehicle). The computing device maygenerate and store the density data for use in determining an insurancequote for the target vehicle. In some aspects, the computing device maybe a computing device within the target vehicle. The target vehicle mayhave one or more sensors used to determine (e.g., sense) the number ofvehicles near the target vehicle. Exemplary sensors were previouslydiscussed with reference to FIG. 2, and may include one or more of thefollowing types of sensors: a camera, a proximity sensor, avehicle-to-vehicle (V2V) communication device, and avehicle-to-infrastructure (V2I) communication device.

In some aspects, the computing device may determine the number ofvehicles based on a number of interactions, such as communications,between the target vehicle and other vehicles (e.g., via V2Vcommunication devices) over a predetermined period of time. The targetvehicle may also determine the number of completely autonomous vehicles,semi-autonomous vehicles, and/or non-autonomous vehicles based on theV2V communications (or lack thereof). For example, other vehicles maycommunicate vehicle identifying information to the target vehicle, suchas make, model, year, automated features it has, VIN, etc. Based on thereceived information, the target vehicle may determine whether the othervehicle is completely autonomous, semi-autonomous, and/ornon-autonomous. In some aspects, if another vehicle does not communicatewith the target vehicle, the target vehicle may determine that the othervehicle is a non-autonomous vehicle. This information may be used togenerate the insurance quote for the vehicle, as will be described infurther detail in the examples that follow.

The computing device may also determine the number of vehicles from datastored by road infrastructure. For example, the road infrastructure maystore a count of the number of vehicles that have passed a particularpoint on the road. When the target vehicle drives within a predetermineddistance from the road infrastructure, the target vehicle maycommunicate with the road infrastructure (e.g., via V2I communicationdevices) and receive the vehicle count from the road infrastructure. Thecomputing device may also determine the number of vehicles based onother devices capable of being used to track location, such as mobilephones and/or other Global Positioning Satellite (GPS) devices.Accordingly, the computing device may determine vehicle count and/orvehicle density independent from an absolute geographical location ofthe vehicle. In other words, geography might not be a factor indetermining the insurance quote.

The computing device may additionally or alternatively determine thenumber of vehicles near the target vehicle and/or otherwise generatevehicle density data based on one or more databases, such as third partydatabases. For example, the computing device may retrieve data from anavigation tool database, such as a GOOGLE MAPS database, and determinethe number of vehicles or vehicle density near the target vehicle basedon the retrieved data. In some aspects, data from the vehicle's sensorsand data retrieved from a database may be combined to determine thenumber of vehicles and/or density data.

Vehicle density may be based on the number of vehicles within apredetermined distance from the target vehicle. For example, the targetvehicle may use its sensors to monitor the number of vehicles within apredetermined radius (e.g., 30 feet) of the target vehicle. Additionallyor alternatively, the predetermined distance may be a predetermineddistance of the target vehicle along a path of the target vehicle. Forexample, if the target vehicle is on a highway, the density measured maybe from the vehicle's location to a predetermined distance in front ofthe vehicle along the highway (e.g., 1 mile in front of the vehicle)and/or a predetermined distance behind the vehicle along the highway(e.g., 0.5 miles behind the vehicle).

In step 804, the vehicle computing device may determine whether new datais available, and in step 806, the vehicle computing device may send thevehicle count or density data to a driving analysis computing device ifnew data is available. The data may be transmitted periodically (e.g.,every 5 days), occasionally, and/or in real time (e.g., while thevehicle is on the road and new data is detected). In step 808, thedriving analysis computing device may receive the vehicle count ordensity data from the vehicle computing device. Steps 804, 806, and 808may be optional if, for example, the driving analysis computing devicereceives the data from other sources, such as directly from the roadinfrastructure or from another database. In step 810, the drivinganalysis computing device may optionally determine whether to generatean insurance quote (or other property of an insurance policy) for thetarget vehicle.

The computing device may determine the density of vehicles near thetarget vehicle based on the vehicle number data received from thevehicle, road infrastructure, and/or various databases. In some aspects,the computing device may determine vehicle density based on vehicletype. In step 812, the computing device may determine the density ofcompletely autonomous vehicles near (e.g., within a predetermineddistance from) the target vehicle. In step 814, the computing device maydetermine the density of semi-autonomous vehicles near the targetvehicle. In step 816, the computing device may determine the density ofnon-autonomous vehicles near the target vehicle.

In step 818, the computing device may generate an insurance quote (whichmay comprise a premium amount) for the target vehicle based on thedetermined density of vehicles. In some aspects, the computing devicemay determine the average and/or median density of vehicles over aparticular period of time (e.g., 1 month, 3 months, etc.). The quote maybe higher if the target vehicle typically travels in a denser area, suchas New York City, and lower if the vehicle typically travels in a lessdense area, such as Perry, Iowa. The computing device may makeadjustments to the quote based on a comparison of the density ofcompletely autonomous vehicles, the density of semi-autonomous vehicles,and/or the density of non-autonomous vehicles. For example, the quotemay be higher if the density of non-autonomous vehicles is higher (e.g.,above a threshold amount). Alternatively, the quote may be lower if thedensity of completely autonomous or semi-autonomous vehicles is higher(e.g., above a threshold amount). The computing device may also applydifferent weights to each of the three vehicle density numbers. The useof weights was previously discussed with reference to FIGS. 6 and 7. Insome aspects, the quote may also be based at least in part on the levelof autonomy of the target vehicle, as also discussed with reference toFIGS. 6 and 7. For example, the quote may be increased if the targetvehicle is non-autonomous or decreased if the target vehicle issemi-autonomous or completely autonomous.

In step 820, the computing device may send the vehicle insurance quoteto the driver or owner of the target vehicle and/or an agent of theinsurance provider. The computing device may return to step 808 to waitfor updated vehicle density or count data.

FIG. 9 is a flow diagram illustrating an example method of analyzing useof autonomous vehicle features and/or maintenance of autonomous vehiclesaccording to one or more aspects of the disclosure. The stepsillustrated in FIG. 9 may be performed by one or more computing device101. For example, a vehicle computing device (e.g., driving analysiscomputer 214 or vehicle control computer 217) and/or a driving analysiscomputing device (e.g., driving analysis computer 251) may perform oneor more of the steps illustrated in FIG. 9. As will be described infurther detail in the examples below, the computing device may generatean insurance quote or reward based on one or more factors, including thedriver's response to switching between an autonomous driving feature(e.g., autonomous steering) and a manual driving feature (e.g., manualsteering).

In step 902, the computing device may track a driver's reaction to avehicle switching between its autonomous driving features (or autonomousdriving modes) and its manual driving features (or manual drivingmodes). A vehicle may have several different gradations of drivingmodes, between fully autonomous and fully manual. Each driving mode maybe determined based on the autonomous features that are available oractive. For example, a manual driving mode may have no autonomousfeatures. A first autonomous driving mode may have autonomous features Aand B active. A second autonomous driving mode more autonomous than thefirst autonomous driving mode may have autonomous features A, B, and Cactive. In a fully autonomous driving mode, the vehicle may have all (ora substantial number) of its autonomous features active, such asautonomous features A, B, C, D, E, and F. Any number of gradations maybe available, and the computing device may determine the mode based on adetection of which autonomous features of the vehicle are active (oravailable).

Each driving mode may additionally or alternatively be aspecific-purpose driving mode. For example, on autonomous mode may be anautonomous parking mode. In this mode, the vehicle's autonomous parkingfeature (e.g., parallel parking or other type of parking) may beactivated. In a highway driving autonomous mode, the vehicle'sautonomous steering control and autonomous speed control features may beactivated. In a collision avoidance autonomous mode, the vehicles'autonomous braking and autonomous steering features may be activated.Any number of driving modes with different autonomous features activatedmay be available.

Switching between the two features or modes may be beneficial for avariety of reasons. For example, the driver might not have experiencewith one or more of the autonomous driving features of the vehicle. Theautonomous driving features may gradually be activated over time as thedriver becomes accustomed to autonomous driving. A vehicle may alsoswitch between autonomous and manual features based on present roadconditions. For example, autonomous steering and speed control may bemore beneficial if the vehicle density is very low where the driver isprone to dozing off. Alternatively, manual steering and speed controlmay be more beneficial if the vehicle density is very high, and thedriver can quickly react to changing traffic conditions. Furthermore,one or more autonomous feature may be turned off in order to teach andmaintain the driver's driving skills.

In some aspects, one or more autonomous driving feature may be switchedoff. For example, a computing device, which may be within the vehicle,may send an instruction to the vehicle to switch off an autonomousdriving feature. The driver may be notified that the autonomous drivingfeature has been switched off by, for example, an audio, visual, and/ortactile notification inside the vehicle. The computing device maydetermine the driver's response to the autonomous driving feature beingswitched off. Additionally or alternatively, the computing device maydetermine a history of the driver's response to the autonomous drivingfeature being switched off over a period of time. For example, thefeature may be switched off multiple times, and the driver's response toeach of those instances may be tracked.

The driver's reaction time may be monitored. For example, if autonomousspeed control is turned off, the amount of time it takes for the driverto maintain or get back up to the speed that the vehicle was travelingat with autonomous speed control active may be tracked. As anotherexample, the vehicle may switch off its autonomous parallel parkingfeature. The amount of time it takes for the driver to parallel park thevehicle may be monitored.

In step 904, the computing device may track the maintenance history ofthe vehicle. For example, the driver's response time to maintenancenotifications may be monitored. Exemplary, non-limiting maintenancenotifications include a check engine notification, an oil levelnotification, a low fuel notification, a software upgrade availabilitynotification, a vehicle hardware or software hack notification, and thelike. For example, if the driver is notified that a software upgrade forthe vehicle (or a component in the vehicle) becomes available, thecomputing device may track how long it takes for the driver to begindownloading and/or installing the software upgrade. Similarly, if thevehicle learns that it (or a particular vehicle component) has beenhacked, the computing device may track how long it takes for the driverto remediate the hack by, for example, bringing the vehicle to a servicelocation, downloading a patch to prevent future hacks, downloadinganti-hacking or anti-virus software, and/or replacing the hackedcomponent. In some aspects, the insurance provider may also provideupdates to the vehicle's security software. These updates may beautomatically pushed to the vehicle. The insurance provider may chargean additional premium for the updates.

In step 906, the computing device may track the vehicle's use ofautonomous vehicle lanes and/or manual vehicle lanes. Certain roads mayinclude lanes dedicated to autonomous vehicles, lanes dedicated tonon-autonomous vehicles, and/or lanes available to both autonomousvehicles and non-autonomous vehicles. As will be described in furtherdetail in the examples below, the driver's insurance quote may bedecreased and/or the driver may be given discounts or other rewards themore often the driver uses the autonomous vehicle lanes. This may bebeneficial to both the insurance provider and the driver because theautonomous vehicle lane may be safer than the non-autonomous vehiclelane in some environments.

In step 908, the computing device may track the driver's use of otherautonomous features, such as autonomous parallel parking. The more oftenthe driver uses an autonomous feature, the greater the driver's discountmay be. For example, if the driver uses autonomous parallel parking atleast twice per week, a $10.00 discount on the insurance premium may beprovided. If the driver uses autonomous parallel parking at least onceper week, a $5.00 discount may be provided.

In step 910, the computing device may optionally determine whether thereis any new data from the vehicle's computing device. If not (step 910:N), the vehicle's computing device may continue to track and store thevehicle operational data previously discussed. In step 912, a drivinganalysis computing device (which may be located remotely from thevehicle's computing device) may receive the monitored data from thevehicle's computing device.

In step 914, the computing device may optionally determine whether togenerate a quote for the driver based on the monitored data. Forexample, the computing device may receive a request to generate a quote.In step 922, the computing device may generate an insurance quote (orother property of an insurance policy) for the vehicle. The insurancequote may be based on one or more of the driver's response to anautonomous feature being switched on or off, the driver's response tomaintenance notifications, the driver's use of autonomous ornon-autonomous vehicle lanes, and/or the driver's use of otherautonomous features (e.g., autonomous parallel parking). The data usedto generate the quotes may be based on the information tracked andstored in steps 902, 904, 906, and/or 908. As previously discussed, thequote may be based on a score determined for the vehicle or driver. Thescore may be based on one or more values for each piece of dataconsidered as well as one or more weights assigned to those pieces ofdata. In step 924, the computing device may send the quote to the driveror to an agent of the insurance provider.

Other factors may be considered to determine the insurance quote. Forexample, the computing device may determine whether and how often thevehicle is driven off peak. Furthermore, the quote may be based on arecord of incidents tracked by the vehicle's sensors or computingdevice, such as the driver momentarily veering out of a lane, thedistance between the target vehicle and a vehicle in front of the targetvehicle, how hard the driver brakes, or any other factors. The quote maybe reduced if the driver does not have any incidents or has a limitednumber of incidents during a predetermined period of time that thedriver's habits are tracked.

The computing device may determine a reward, such as a discount on apremium or deductible, alternatively to or additionally to a quote in asimilar manner as it determines the quote (e.g., based on one or more ofthe factors previously discussed). In particular, in step 916, thecomputing device may determine whether to generate a reward for thedriver based on the monitored data, and in step 918, the computingdevice may generate the reward based on the monitored data. A reward maybe generated in lieu of the quote if the driver is, for example, alreadya customer of the insurance provider. In step 920, the computing devicemay send the reward to the driver or to an agent of the insuranceprovider.

FIG. 10 is a flow diagram illustrating an example method of enablingvehicle teaching features and/or monitoring the driver's response toteaching features according to one or more aspects of the disclosure.The steps illustrated in FIG. 10 may be performed by one or morecomputing device 101. For example, a vehicle computing device (e.g.,driving analysis computer 214 or vehicle control computer 217) and/or adriving analysis computing device (e.g., driving analysis computer 251)may perform one or more of the steps illustrated in FIG. 10. If a driveruses an autonomous vehicle, the driver may lose some of his or herdriving skills over time. As will be described in further detail in theexamples below, a vehicle may include a teaching feature that disablesone or more autonomous driving features for a predetermined period oftime. During this period of time, the driver may manually drive thevehicle, and the vehicle may provide instructions to the driver to teachand/or help the driver maintain his or her driving skills.

In step 1002, the computing device may determine whether to enable theteaching feature of the autonomous vehicle. The teaching feature may beenabled by the vehicle's computing device and/or the insuranceprovider's computing device. In some aspects, the driver may enable theteaching feature in step 1002 by providing an input to the vehicle'scomputing device. In step 1004, the vehicle's computing device notifiesthe driver that the teaching feature has been enabled, such as byproviding feedback or confirmation to the driver that the teachingfeature has been enabled. For example, the vehicle computing device maygenerate an audio, visual, and/or tactile prompt to the driver that theteaching feature has been enabled. In some aspects, a teaching featurelight or icon on the car's graphical user interface may display anindication that the teaching feature is enabled. The teaching featuremay also be disabled at any time, such as if the driver instructs thevehicle to disable the teaching feature.

In step 1006, the computing device may monitor current conditions of thevehicle or the vehicle's environment, and in step 1008, the computingdevice may determine whether to initiate the teaching feature based oncurrent conditions (e.g., if a criterion is satisfied). If the computingdevice determines to initiate the teaching feature (step 1008: Y), thecomputing device in step 1010 may notify the driver of the vehicle thatthe teaching feature is or will be initiated. In step 1012, the teachingfeature may be initiated, such as by switching off an autonomous vehiclefeature. Non-limiting examples of steps 1006, 1008, 1010, and/or 1012will now be provided.

In step 1006, the vehicle computing device may monitor the density ofvehicles within a predetermined distance from the vehicle, as previouslydiscussed with reference to FIG. 8. If the vehicle density drops below athreshold, the computing device may determine to initiate the teachingfeature in step 1008, and notify the driver, in step 1010, that theteaching feature is or will be initiated. If the driver is notified instep 1010, the computing device may delay initiating the teachingfeature for a predetermined amount of time, such as 5 seconds, to givethe driver time to prepare. The driver may also be notified by visual,audio, and/or tactile information that the teaching feature will beinitiated in 5 seconds. For example, the vehicle may display (or audiblyspeak) a countdown to initiation of the teaching feature. In step 1012,the computing device may initiate the teaching feature. For example, thecomputing device may switch off autonomous steering or autonomous speedcontrol for a predetermined period of time (e.g., 10 minutes).

The vehicle's teaching feature may also be used to teach the driver howto react in particular conditions or environments. For example, thedriver's merging skills may be kept up to date by switching offautonomous driving before the vehicle enters onto a ramp. The computingdevice may determine the vehicle's location and/or route, such as bytracking the driver's location using his or her mobile phone or anyother location tracking mechanism. If the driver's current route willtake the driver onto a ramp, the computing device may notify the driverthat one or more autonomous driving feature will be switched off priorto the vehicle entering the ramp. The driver's ability to handle thevehicle in any other driving situation (e.g., switching lanes,bumper-to-bumper traffic, turning, etc.) may similarly be tested orrefreshed by the computing device in a similar manner.

In step 1014, the computing device may monitor the driver's reaction(e.g., response) to the teaching feature. For example, the computingdevice may determine and/or store the driver's reaction to theautonomous steering or autonomous speed control being switched off. Thecomputing device may determine the amount of time that the driver isbelow the speed limit and the amount of time that the driver is abovethe speed limit during the teaching period. The computing device mayalso track the number of times that the driver erroneously crosses adriving lane marking or divider (e.g., if the driver veers across a lanemarker without intending to switch lanes or drives onto a shoulder ofthe road). If the driver is being taught how to merge, the computingdevice may determine, for example, the number of times that the driverbrakes during his or her merge.

In step 1016, the computing device may determine whether the driver'sreaction to the teaching feature is unsafe at any point during theteaching period. If the driver's reaction is unsafe (step 1016: Y), thecomputing device may disable the teaching feature by, for example,reactivating the automated driving feature in step 1020. The driver'sreaction may be unsafe if the driver's reaction exceeds a threshold. Forexample, if the driver's speed exceeds the posted speed limit by apredetermined amount, such as 20 miles per hour over the speed limit,the computing device may reactivate autonomous speed control if it wasdeactivated for teaching. As another example, if the driver swerves intoanother lane or onto the shoulder by a predetermined distance, such astwo feet, the computing device may reactivate autonomous steering if itwas deactivated for teaching. As yet another example, if the driverbrakes too hard (which may be measured by the amount of force applied tothe brakes and/or the amount of deceleration of the vehicle), thecomputing device may reactivate autonomous driving if it was deactivatedto refresh the driver's merging skills.

In step 1018, the computing device may determine whether the conditionof the vehicle or the vehicle's environment is no longer safe forteaching (e.g., that the condition originally satisfied in step 1008 isno longer satisfied). If the environment is unsafe for teaching, thecomputing device may disable the teaching feature in step 1020. Forexample, the current conditions might not be safe for teaching if thevehicle density exceeds a predetermined threshold. The threshold may bethe same as the threshold used to initiate the teaching feature in step1008, or the thresholds may be different.

In step 1022, the computing device may determine whether the teachingperiod has ended. For example, the teaching period may last apredetermined amount of time after initiation of the teaching feature,such as 10 minutes. Alternatively, the teaching period may end when thedriving action taught to the driver has ended, such as when the driverhas successfully merged onto a highway. If the teaching period has notended (step 1022: N), the computing device may continue to monitor foran unsafe response in step 1016 and/or unsafe conditions in step 1018.

If the teaching period has ended (step 1022: Y), the computing device,in step 1024, may generate a score based on the driver's reaction(s)during the teaching period. As previously discussed, the score may bebased on one or more values and/or weights. For example, the score maybe on a scale of 0 to 100, with 100 being the best or preferredresponse. The computing device may generate a score of 100 if thevehicle does not detect any mistakes during the teaching period. Forexample, the driver may receive a score of 100 if the vehicle does notexceed the speed limit (or a predetermined amount above the speed limit,such as 5 miles per hour above the speed limit) with the autonomousspeed control switched off. Deductions may be made each time the driverexceeds the speed limit and/or based on a comparison of the amount oftime that the driver spends above the speed limit and an amount of timethat the driver spends at or below the speed limit. For example, if theteaching period is 10 minutes, and the vehicle spends 7 minutes at orbelow the speed limit, the computing device may generate a score of 70%(e.g., 7/10).

As another example, the driver may receive a score of 100 if the vehicledoes not cross onto the shoulder of the road with autonomous steeringswitched off. Deductions may be made for each instance of the vehiclecrossing onto the shoulder and/or based on a comparison of the amount oftime the vehicle spends in driving lanes and the amount of time thevehicle spends at least partially in the shoulder lane. As yet anotherexample, the driver may receive a score of 100 if the vehicle does notbrake at all when the driver's merging skills are tested. Deductions maybe made each time the driver applies the brakes.

In step 1026, the computing device may determine whether the scoregenerated in step 1024 exceeds one or more score threshold. In someexamples, the threshold may determine whether the driver's response wassufficient or not. In step 1028, the computing device may provide areward to the driver if the driver's score exceeds the threshold (step1026: Y). Exemplary rewards were previously described, and may include,for example, a reduction in an insurance premium, a gift, such as a freemovie rental or free gas, or any other reward.

On the other hand, if the driver's score does not exceed the threshold(step 1026: N), the computing device, in step 1030, may provide one ormore recommendations to the driver to help the driver improve or refreshhis or her driving skills In some aspects, the recommendation may begiven to the driver while the driver is still in the vehicle. Forexample, a voice prompt may recommend that the driver slow down nexttime if the driver spent too much time above the speed limit withautonomous speed control deactivated. Other recommendations may beprovided based on which autonomous feature was deactivated. Therecommendations may additionally or alternatively be provided to thedriver visually, such as on a display of the vehicle (e.g., an LCDscreen, a heads-up-display (HUD), and the like).

As previously discussed with reference to FIG. 9, the computing devicemay also determine an amount of a property of an insurance policy forthe vehicle, such as an insurance premium amount, based on the scoregenerated in step 1024.

A new insurance policy may be created for semi-autonomous or completelyautonomous vehicles. In particular, traffic accidents may be less andless likely caused by the driver as vehicles utilize more and moreautonomous features. As such, liability may be allocated amount multipleparties, including (1) the driver, (2) manufacturers (or servicers) ofmalfunctioning autonomous features (e.g., malfunctioning autonomoussteering, braking, and/or speed control), (3) manufacturers (orservicers) of malfunctioning roadway infrastructure or malfunctioningV2I or I2V communication systems, and/or (4) third parties thatillegally accesses (e.g., hack) the vehicle's autonomous driving system.

In some aspects, the driver may be primarily liable if the driveroverrides the autonomous system or automated system warnings and causesan accident by initiating manual driving features. The driver might notbe primarily liable in certain situations, such as if the teachingfeature previously described is automatically enabled and initiated(e.g., without the driver's input). The manufacturer of an autonomousfeature (e.g., autonomous steering) may be liable if the autonomousfeature malfunctions and causes an accident. No-fault coverage may beused if an “Act of God,” such as severe weather or other interference,confuses the sensors in the vehicle, causing an accident duringautonomous driving mode.

While the aspects described herein have been discussed with respect tospecific examples including various modes of carrying out aspects of thedisclosure, those skilled in the art will appreciate that there arenumerous variations and permutations of the above described systems andtechniques that fall within the spirit and scope of the invention.

What is claimed is:
 1. A method comprising: determining whether adensity of vehicles within a predetermined distance of a vehicle isbelow a threshold density of vehicles; in response to a determinationthat the density of vehicles within the predetermined distance of thevehicle is below the threshold density of vehicles, determining toswitch off an autonomous feature of the vehicle; in response to thedetermining to switch off the autonomous feature of the vehicle, sendingone or more of visual, audio, and/or tactile information indicating thatthe autonomous feature of the vehicle will be switched off after apredetermined time period; after the predetermined time period, sending,by a computing device and to the vehicle, computer-executableinstructions to switch off the autonomous feature of the vehicle;determining, by the computing device, operational data of the vehicleafter the autonomous feature of the vehicle is switched off, wherein theoperational data of the vehicle indicates a response, of a driver of thevehicle, to the autonomous feature of the vehicle being switched off;after the autonomous feature of the vehicle is switched off and within apredetermined teaching time period after the autonomous feature of thevehicle is switched off, determining that the response, of the driver ofthe vehicle, does not exceed a threshold response during thepredetermined teaching time period; after the predetermined teachingtime period expires and based on the determining that the response, ofthe driver of the vehicle, does not exceed the threshold response duringthe predetermined teaching time period, maintaining the autonomousfeature of the vehicle to be switched off and generating a score basedon the operational data of the vehicle; and after the predeterminedteaching time period expires, after the maintaining the autonomousfeature of the vehicle to be switched off, and based on determining thatthe response, of the driver of the vehicle, exceeds the thresholdresponse, reactivating the autonomous feature of the vehicle.
 2. Themethod of claim 1, further comprising: if the score exceeds a thresholdscore, providing a reward to the driver of the vehicle.
 3. The method ofclaim 1, further comprising: if the score does not exceed a thresholdscore, generating a recommended response for the driver of the vehicle.4. The method of claim 3, further comprising: providing the recommendedresponse to the driver of the vehicle while the driver is in thevehicle.
 5. The method of claim 1, further comprising: prior toswitching off the autonomous feature of the vehicle, determining thatthe driver of the vehicle has enabled a teaching feature of the vehicle.6. The method of claim 1, further comprising: in response to adetermination that the density of vehicles within the predetermineddistance of the vehicle is below the threshold density of vehicles at afirst time but is not below the threshold density of vehicles at asecond time after the first time, switching on the autonomous feature ofthe vehicle.
 7. The method of claim 1, further comprising: determining avalue of a property of an insurance policy for the vehicle based on thegenerated score.
 8. The method of claim 1, wherein the thresholdresponse is based on one or more of a speed limit, deviation from a lanewhich the vehicle is intended to use, an amount of force applied tobrakes of the vehicle, or an amount of deceleration of the vehicle.
 9. Asystem comprising: a vehicle configured to operate with an autonomousfeature; and a driving analysis computing device comprising: aprocessor; and memory storing computer-executable instructions that,when executed by the processor, cause the driving analysis computingdevice to: determine whether a density of vehicles within apredetermined distance of the vehicle is below a threshold density ofvehicles; in response to a determination that the density of vehicleswithin the predetermined distance of the vehicle is below the thresholddensity of vehicles, determine to switch off an autonomous feature ofthe vehicle, in response to the determining to switch off the autonomousfeature of the vehicle, send one or more of visual, audio, and/ortactile information indicating that the autonomous feature of thevehicle will be switched off after a predetermined time period; afterthe predetermined time period, send a computer-executable instruction tothe vehicle to switch off the autonomous feature of the vehicle; anddetermine operational data of the vehicle after the autonomous featureof the vehicle is switched off, wherein the operational data of thevehicle indicates a response, of a driver of the vehicle, to theautonomous feature of the vehicle being switched off; and after theautonomous feature of the vehicle is switched off and within apredetermined teaching time period after the autonomous feature of thevehicle is switched off, determine that the response, of the driver ofthe vehicle, does not exceed a threshold response during thepredetermined teaching time period; after the predetermined teachingtime period expires and based on the determining that the response, ofthe driver of the vehicle, does not exceed the threshold response duringthe predetermined teaching time period, maintain the autonomous featureof the vehicle to be switched off and generate a score based on theoperational data of the vehicle after the autonomous feature is switchedoff; and after the predetermined teaching time period expires, after themaintaining the autonomous feature of the vehicle to be switched off,and based on determining that the response, of the driver of thevehicle, exceeds the threshold response, reactivate the autonomousfeature of the vehicle.
 10. The system of claim 9, wherein the memorystores computer-executable instructions that, when executed by theprocessor, cause the driving analysis computing device to: if the scoreexceeds a threshold score, provide a reward to the driver of thevehicle.
 11. The system of claim 9, wherein the memory storescomputer-executable instructions that, when executed by the processor,cause the driving analysis computing device to: if the score does notexceed a threshold score, generate a recommended response for the driverof the vehicle.
 12. The system of claim 9, wherein the thresholdresponse is based on one or more of a speed limit, deviation from a lanewhich the vehicle is intended to use, an amount of force applied tobrakes of the vehicle, or an amount of deceleration of the vehicle. 13.A non-transitory computer readable medium storing instructions that,when read by a computing device, cause the computing device to:determine whether a density of vehicles within a predetermined distanceof a vehicle is below a threshold density of vehicles; in response to adetermination that the density of vehicles within the predetermineddistance of the vehicle is below the threshold density of vehicles,determine to switch off an autonomous feature of the vehicle; inresponse to the determining to switch off the autonomous feature of thevehicle, send one or more of visual, audio, and/or tactile informationindicating that the autonomous feature of the vehicle will be switchedoff after a predetermined time period; after the predetermined timeperiod, send a computer-executable instruction to the vehicle to switchoff the autonomous feature of the vehicle; determine operational data ofthe vehicle after the autonomous feature of the vehicle is switched off,wherein the operational data of the vehicle indicates a response, of adriver of the vehicle, to the autonomous feature of the vehicle beingswitched off; after the autonomous feature of the vehicle is switchedoff and within a predetermined teaching time period after the autonomousfeature of the vehicle is switched off, determine that the response, ofthe driver of the vehicle, does not exceed a threshold response duringthe predetermined teaching time period; after the predetermined teachingtime period expires and based on the determining that the response, ofthe driver of the vehicle, does not exceed the threshold response duringthe predetermined teaching time period, maintain the autonomous featureof the vehicle to be switched off and generate a score based on theoperational data of the vehicle after the autonomous feature is switchedoff; and after the predetermined teaching time period expires, after themaintaining the autonomous feature of the vehicle to be switched off,and based on determining that the response, of the driver of thevehicle, exceeds the threshold response, reactivate the autonomousfeature of the vehicle.
 14. The non-transitory computer readable mediumof claim 13, storing instructions that, when read by the computingdevice, further cause the computing device to: if the score exceeds athreshold score, provide a reward to the driver of the vehicle.
 15. Thenon-transitory computer readable medium of claim 13, storinginstructions that, when read by the computing device, further cause thecomputing device to: if the score does not exceed a threshold score,generate a recommended response for the driver of the vehicle.
 16. Thenon-transitory computer readable medium of claim 15, storinginstructions that, when read by the computing device, further cause thecomputing device to: provide the recommended response to the driver ofthe vehicle while the driver is in the vehicle.
 17. The non-transitorycomputer readable medium of claim 13, storing instructions that, whenread by the computing device, further cause the computing device to:prior to switching off the autonomous feature of the vehicle, generate anotification for the driver of the vehicle that the autonomous featureof the vehicle will be switched off.
 18. The non-transitory computerreadable medium of claim 13, wherein the non-transitory computerreadable medium stores instructions that, when read by the computingdevice, further cause the computing device to: in response to adetermination that the density of vehicles within the predetermineddistance is below the threshold density of vehicles at a first time butis not below the threshold density of vehicles at a second time afterthe first time, switch on the autonomous feature of the vehicle.
 19. Thenon-transitory computer readable medium of claim 13, storinginstructions that, when read by the computing device, further cause thecomputing device to: determine a value of a property of an insurancepolicy for the vehicle based on the generated score.
 20. Thenon-transitory computer readable medium of claim 13, wherein thethreshold response is based on one or more of a speed limit, deviationfrom a lane which the vehicle is intended to use, an amount of forceapplied to brakes of the vehicle, or an amount of deceleration of thevehicle.